TY - CHAP A1 - Weber, Karsten ED - Grimm, Petra ED - Zöllner, Oliver T1 - Künstliche Intelligenz am Ende des Lebens BT - Szenarien des KI-Einsatzes zur Prognose und Gestaltung des Sterbens T2 - Ethik der Digitalisierung in Gesundheitswesen und Pflege: Analysen und ein Tool zur integrierten Forschung Y1 - 2025 SN - 978-3-515-13552-8 U6 - https://doi.org/10.25162/9783515135528 SP - 87 EP - 107 PB - Franz Steiner Verlag CY - Stuttgart ER - TY - GEN A1 - Scheppach, Markus W. A1 - Rauber, David A1 - Zingler, C. A1 - Weber Nunes, Danilo A1 - Probst, Andreas A1 - Römmele, Christoph A1 - Nagl, Sandra A1 - Ebigbo, Alanna A1 - Palm, Christoph A1 - Messmann, Helmut T1 - Instrumentenerkennung während der endoskopischen Submukosadissektion mittels künstlicher Intelligenz T2 - Zeitschrift für Gastroenterologie N2 - Einleitung: Die endoskopische Submukosadissektion (ESD) ist eine komplexe Technik zur Resektion gastrointestinaler Frühneoplasien. Dabei werden für die verschiedenen Schritte der Intervention spezifische endoskopische Instrumente verwendet. Die präzise und automatische Erkennung und Abgrenzung der verwendeten Instrumente (Injektionsnadeln, elektrochirurgische Messer mit unterschiedlichen Konfigurationen, hämostatische Zangen) könnte wertvolle Informationen über den Fortschritt und die Verfahrensmerkmale der ESD liefern und eine automatische standardisierte Berichterstattung ermöglichen. Ziele: Ziel dieser Studie war die Entwicklung eines KI-Algorithmus zur Erkennung und Delineation von endoskopischen Instrumenten bei der ESD. Methodik: 17 ESD-Videos (9×rektal, 5×ösophageal, 3×gastrisch) wurden retrospektiv zusammengestellt. Auf 8530 Einzelbilder dieser Videos wurden durch 2 Studienmitarbeiter die folgenden Klassen eingezeichnet: Hakenmesser – Spitze, Hakenmesser – Katheter, Nadelmesser – Spitze und – Katheter, Injektionsnadel -Spitze und – Katheter sowie hämostatische Zange – Spitze und – Katheter. Der annotierte Datensatz wurde zum Training eines DeepLabV3+-Deep-Learning-Algorithmus mit ConvNeXt-Backbone zur Erkennung und Abgrenzung der genannten Klassen verwendet. Die Evaluation erfolgte durch 5-fache interne Kreuzvalidierung. Ergebnis: Die Validierung auf Einzelpixelbasis ergab insgesamt einen F1-Score von 0,80, eine Sensitivität von 0,81 und eine Spezifität von 1,00. Es wurden F1-Scores von 1,00, 0,97, 0,80, 0,98, 0,85, 0,97, 0,80, 0,51 bzw. 0,85 für die Klassen Hakenmesser – Katheter und – Spitze, Nadelmesser – Katheter und – Spitze, Injektionsnadel – Katheter und – Spitze, hämostatische Zange – Katheter und – Spitze gemessen. Schlussfolgerung: In dieser Studie wurden die wichtigsten endoskopischen Instrumente, die während der ESD verwendet werden, mit hoher Genauigkeit erkannt. Die geringere Leistung bei der hämostatische Zange – Katheter kann auf die Unterrepräsentation dieser Klassen in den Trainingsdaten zurückgeführt werden. Zukünftige Studien werden sich auf die Erweiterung der Instrumentenklassen sowie auf die Ausbalancierung der Trainingsdaten konzentrieren. Y1 - 2025 U6 - https://doi.org/10.1055/s-0045-1811092 VL - 63 IS - 8 PB - Thieme ER - TY - CHAP A1 - Klausmann, Leonard A1 - Rueckert, Tobias A1 - Rauber, David A1 - Maerkl, Raphaela A1 - Yildiran, Suemeyye R. A1 - Gutbrod, Max A1 - Palm, Christoph T1 - DIY challenge blueprint: from organization to technical realization in biomedical image analysis T2 - Medical Image Computing and Computer Assisted Intervention - MICCAI 2025 ; Proceedings Part XI N2 - Biomedical image analysis challenges have become the de facto standard for publishing new datasets and benchmarking different state-of-the-art algorithms. Most challenges use commercial cloud-based platforms, which can limit custom options and involve disadvantages such as reduced data control and increased costs for extended functionalities. In contrast, Do-It-Yourself (DIY) approaches have the capability to emphasize reliability, compliance, and custom features, providing a solid basis for low-cost, custom designs in self-hosted systems. Our approach emphasizes cost efficiency, improved data sovereignty, and strong compliance with regulatory frameworks, such as the GDPR. This paper presents a blueprint for DIY biomedical imaging challenges, designed to provide institutions with greater autonomy over their challenge infrastructure. Our approach comprehensively addresses both organizational and technical dimensions, including key user roles, data management strategies, and secure, efficient workflows. Key technical contributions include a modular, containerized infrastructure based on Docker, integration of open-source identity management, and automated solution evaluation workflows. Practical deployment guidelines are provided to facilitate implementation and operational stability. The feasibility and adaptability of the proposed framework are demonstrated through the MICCAI 2024 PhaKIR challenge with multiple international teams submitting and validating their solutions through our self-hosted platform. This work can be used as a baseline for future self-hosted DIY implementations and our results encourage further studies in the area of biomedical image analysis challenges. KW - Biomedical challenges KW - Image analysis KW - Blueprint KW - Do-It-Yourself KW - Self-hosting Y1 - 2025 SN - 978-3-032-05141-7 U6 - https://doi.org/10.1007/978-3-032-05141-7_9 SP - 85 EP - 95 PB - Springer CY - Cham ER - TY - INPR A1 - Gutbrod, Max A1 - Rauber, David A1 - Weber Nunes, Danilo A1 - Palm, Christoph T1 - OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution Detection N2 - The growing reliance on Artificial Intelligence (AI) in critical domains such as healthcare demands robust mechanisms to ensure the trustworthiness of these systems, especially when faced with unexpected or anomalous inputs. This paper introduces the Open Medical Imaging Benchmarks for Out-Of-Distribution Detection (OpenMIBOOD), a comprehensive framework for evaluating out-of-distribution (OOD) detection methods specifically in medical imaging contexts. OpenMIBOOD includes three benchmarks from diverse medical domains, encompassing 14 datasets divided into covariate-shifted in-distribution, near-OOD, and far-OOD categories. We evaluate 24 post-hoc methods across these benchmarks, providing a standardized reference to advance the development and fair comparison of OOD detection methods. Results reveal that findings from broad-scale OOD benchmarks in natural image domains do not translate to medical applications, underscoring the critical need for such benchmarks in the medical field. By mitigating the risk of exposing AI models to inputs outside their training distribution, OpenMIBOOD aims to support the advancement of reliable and trustworthy AI systems in healthcare. The repository is available at this https URL. Y1 - 2025 U6 - https://doi.org/10.48550/arXiv.2503.16247 N1 - Der Aufsatz wurde peer-reviewed veröffentlicht und ist ebenfalls in diesem Repositorium verzeichnet unter: https://opus4.kobv.de/opus4-oth-regensburg/8467 ER - TY - GEN A1 - Weber, Karsten T1 - Künstliche Intelligenz in der Verwaltung: Chancen und Risiken Y1 - 2025 ER - TY - GEN A1 - Weber, Karsten T1 - Transparenz aus praktischer und regulatorischer Perspektive: Ein Beitrag zur Technikfolgenabschätzung der KI? Y1 - 2023 ER - TY - GEN A1 - Weber, Karsten T1 - Verantwortung als Leitprinzip der Arbeit der Gemeinsamen Ethikkommission der Hochschulen Bayerns (GEHBa) Y1 - 2023 ER - TY - GEN A1 - Weber, Karsten T1 - Driverless vehicles and software dependency: sustainability, security, and safety are at stake Y1 - 2023 ER - TY - GEN A1 - Weber, Karsten T1 - KI und Sterben Just-in-Time BT - Wenn das Unwägbare voraussehbar und das Unvermeidliche beeinflussbar wird Y1 - 2023 ER - TY - GEN A1 - Weber, Karsten T1 - „Ist da jemand?“ – Die Geschichte des Versuchs denkende Maschinen zu bauen Y1 - 2023 ER - TY - GEN A1 - Weber, Karsten T1 - Social challenges that (might) come from AI Y1 - 2023 ER - TY - JOUR A1 - Weiherer, Maximilian A1 - Eigenberger, Andreas A1 - Egger, Bernhard A1 - Brébant, Vanessa A1 - Prantl, Lukas A1 - Palm, Christoph T1 - Learning the shape of female breasts: an open-access 3D statistical shape model of the female breast built from 110 breast scans JF - The Visual Computer N2 - We present the Regensburg Breast Shape Model (RBSM)—a 3D statistical shape model of the female breast built from 110 breast scans acquired in a standing position, and the first publicly available. Together with the model, a fully automated, pairwise surface registration pipeline used to establish dense correspondence among 3D breast scans is introduced. Our method is computationally efficient and requires only four landmarks to guide the registration process. A major challenge when modeling female breasts from surface-only 3D breast scans is the non-separability of breast and thorax. In order to weaken the strong coupling between breast and surrounding areas, we propose to minimize the variance outside the breast region as much as possible. To achieve this goal, a novel concept called breast probability masks (BPMs) is introduced. A BPM assigns probabilities to each point of a 3D breast scan, telling how likely it is that a particular point belongs to the breast area. During registration, we use BPMs to align the template to the target as accurately as possible inside the breast region and only roughly outside. This simple yet effective strategy significantly reduces the unwanted variance outside the breast region, leading to better statistical shape models in which breast shapes are quite well decoupled from the thorax. The RBSM is thus able to produce a variety of different breast shapes as independently as possible from the shape of the thorax. Our systematic experimental evaluation reveals a generalization ability of 0.17 mm and a specificity of 2.8 mm. To underline the expressiveness of the proposed model, we finally demonstrate in two showcase applications how the RBSM can be used for surgical outcome simulation and the prediction of a missing breast from the remaining one. Our model is available at https://www.rbsm.re-mic.de/. KW - Statistical shape model KW - Non-rigid surface registration KW - Breast imaging KW - Surgical outcome simulation KW - Breast reconstruction surgery Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-30506 N1 - Corresponding author: Christoph Palm N1 - Zugehörige arXiv-Publikation: https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/docId/2023 VL - 39 IS - 4 SP - 1597 EP - 1616 PB - Springer Nature ER - TY - JOUR A1 - Brébant, Vanessa A1 - Weiherer, Maximilian A1 - Noisser, Vivien A1 - Seitz, Stephan A1 - Prantl, Lukas A1 - Eigenberger, Andreas T1 - Implants Versus Lipograft: Analysis of Long-Term Results Following Congenital Breast Asymmetry Correction JF - Aesthetic Plastic Surgery N2 - Aims Congenital breast asymmetry represents a particular challenge to the classic techniques of plastic surgery given the young age of patients at presentation. This study reviews and compares the long-term results of traditional breast augmentation using silicone implants and the more innovative technique of lipografting. Methods To achieve this, we not only captured subjective parameters such as satisfaction with outcome and symmetry, but also objective parameters including breast vol-ume and anthropometric measurements. The objective examination was performed manually and by using the Vectra H2 photogrammetry scanning system. Results Differences between patients undergoing either implant augmentation or lipograft were revealed not to be significant with respect to patient satisfaction with surgical outcome (p= 0.55) and symmetry (p= 0.69). Furthermore, a breast symmetry of 93 % was reported in both groups. Likewise, no statistically significant volume difference between the left and right breasts was observed in both groups (p\0.41). However, lipograft patients needed on average 2.9 procedures to achieve the desired result, compared with 1.3 for implant augmentation. In contrast, patients treated with implant augmentation may require anumber of implant changes during their lifetime. Conclusion Both methods may be considered for patients presenting with congenital breast asymmetry. KW - Congenital Breast Asymmetry KW - 3D volumetry KW - Autologous fat injections KW - Three-dimensional imaging KW - Lipograft Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-32404 N1 - Corresponding author der OTH Regensburg: Vanessa Brébant VL - 46 SP - 2228 EP - 2236 PB - Springer Nature ER - TY - JOUR A1 - Maier, Johannes A1 - Weiherer, Maximilian A1 - Huber, Michaela A1 - Palm, Christoph T1 - Imitating human soft tissue on basis of a dual-material 3D print using a support-filled metamaterial to provide bimanual haptic for a hand surgery training system JF - Quantitative Imaging in Medicine and Surgery N2 - Background: Currently, it is common practice to use three-dimensional (3D) printers not only for rapid prototyping in the industry, but also in the medical area to create medical applications for training inexperienced surgeons. In a clinical training simulator for minimally invasive bone drilling to fix hand fractures with Kirschner-wires (K-wires), a 3D-printed hand phantom must not only be geometrically but also haptically correct. Due to a limited view during an operation, surgeons need to perfectly localize underlying risk structures only by feeling of specific bony protrusions of the human hand. Methods: The goal of this experiment is to imitate human soft tissue with its haptic and elasticity for a realistic hand phantom fabrication, using only a dual-material 3D printer and support-material-filled metamaterial between skin and bone. We present our workflow to generate lattice structures between hard bone and soft skin with iterative cube edge (CE) or cube face (CF) unit cells. Cuboid and finger shaped sample prints with and without inner hard bone in different lattice thickness are constructed and 3D printed. Results: The most elastic available rubber-like material is too firm to imitate soft tissue. By reducing the amount of rubber in the inner volume through support material (SUP), objects become significantly softer. Without metamaterial, after disintegration, the SUP can be shifted through the volume and thus the body loses its original shape. Although the CE design increases the elasticity, it cannot restore the fabric form. In contrast to CE, the CF design increases not only the elasticity but also guarantees a local limitation of the SUP. Therefore, the body retains its shape and internal bones remain in its intended place. Various unit cell sizes, lattice thickening and skin thickness regulate the rubber material and SUP ratio. Test prints with higher SUP and lower rubber material percentage appear softer and vice versa. This was confirmed by an expert surgeon evaluation. Subjects adjudged pure rubber-like material as too firm and samples only filled with SUP or lattice structure in CE design as not suitable for imitating tissue. 3D-printed finger samples in CF design were rated as realistic compared to the haptic of human tissue with a good palpable bone structure. Conclusions: We developed a new dual-material 3D print technique to imitate soft tissue of the human hand with its haptic properties. Blowy SUP is trapped within a lattice structure to soften rubber-like 3D print material, which makes it possible to reproduce a realistic replica of human hand soft tissue. KW - Dual-material 3D printing KW - Hand surgery training KW - Metamaterial KW - Support material KW - Tissue-imitating hand phantom KW - Handchirurgie KW - 3D-Druck KW - Biomaterial KW - Lernprogramm Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-979 N1 - Corresponding author: Christoph Palm VL - 9 IS - 1 SP - 30 EP - 42 PB - AME Publishing Company ER - TY - JOUR A1 - Maier, Johannes A1 - Weiherer, Maximilian A1 - Huber, Michaela A1 - Palm, Christoph T1 - Optically tracked and 3D printed haptic phantom hand for surgical training system JF - Quantitative Imaging in Medicine and Surgery N2 - Background: For surgical fixation of bone fractures of the human hand, so-called Kirschner-wires (K-wires) are drilled through bone fragments. Due to the minimally invasive drilling procedures without a view of risk structures like vessels and nerves, a thorough training of young surgeons is necessary. For the development of a virtual reality (VR) based training system, a three-dimensional (3D) printed phantom hand is required. To ensure an intuitive operation, this phantom hand has to be realistic in both, its position relative to the driller as well as in its haptic features. The softest 3D printing material available on the market, however, is too hard to imitate human soft tissue. Therefore, a support-material (SUP) filled metamaterial is used to soften the raw material. Realistic haptic features are important to palpate protrusions of the bone to determine the drilling starting point and angle. An optical real-time tracking is used to transfer position and rotation to the training system. Methods: A metamaterial already developed in previous work is further improved by use of a new unit cell. Thus, the amount of SUP within the volume can be increased and the tissue is softened further. In addition, the human anatomy is transferred to the entire hand model. A subcutaneous fat layer and penetration of air through pores into the volume simulate shiftability of skin layers. For optical tracking, a rotationally symmetrical marker attached to the phantom hand with corresponding reference marker is developed. In order to ensure trouble-free position transmission, various types of marker point applications are tested. Results: Several cuboid and forearm sample prints lead to a final 30 centimeter long hand model. The whole haptic phantom could be printed faultless within about 17 hours. The metamaterial consisting of the new unit cell results in an increased SUP share of 4.32%. Validated by an expert surgeon study, this allows in combination with a displacement of the uppermost skin layer a good palpability of the bones. Tracking of the hand marker in dodecahedron design works trouble-free in conjunction with a reference marker attached to the worktop of the training system. Conclusions: In this work, an optically tracked and haptically correct phantom hand was developed using dual-material 3D printing, which can be easily integrated into a surgical training system. KW - Handchirurgie KW - 3D-Druck KW - Lernprogramm KW - Zielverfolgung KW - HaptiVisT KW - Dual-material 3D printing KW - hand surgery training KW - metamaterial KW - tissue imitating phantom hand Y1 - 2020 U6 - https://doi.org/10.21037/qims.2019.12.03 N1 - Corresponding author: Christoph Palm VL - 10 IS - 02 SP - 340 EP - 455 PB - AME Publishing Company CY - Hong Kong, China ER - TY - JOUR A1 - Noisser, Vivien A1 - Eigenberger, Andreas A1 - Weiherer, Maximilian A1 - Seitz, Stephan A1 - Prantl, Lukas A1 - Brébant, Vanessa T1 - Surgery of congenital breast asymmetry - which objective parameter influences the subjective satisfaction with long-term results JF - Archives of Gynecology and Obstetrics N2 - Purpose Congenital breast asymmetry is a serious gynecological malformation for affected patients. The condition hits young women in puberty and is associated with socio-esthetic handicap, depression, and psychosexual problems. Surgical treatment is usually early in the patient's lifetime, so a long-term sustainable solution is important. Although postoperative outcome has been evaluated in several studies before, this study is the first to analyze which objective parameters have the greatest influence on subjective satisfaction with long-term results. Methods Thirty-four patients diagnosed with congenital breast asymmetry that underwent either lipofilling or implant therapy between the years of 2008 to 2019 were examined. On average, our collective comprised patients seven years after surgery. Data were mainly gathered through manual measurements, patient-reported outcome measures (Breast Q™), and breast volumetry based on 3D scans (Vectra® H2, Canfield Scientific). Results Among all analyzed parameters, only areolar diameter correlated significantly negatively with the subjective outcome satisfaction of the patient. Regarding the subjective assessment of postoperative satisfaction with similarity of the breasts, again the mean areolar diameter, but also the difference in areolar diameter and breast volume between the right and left breasts correlated significantly negatively. Conclusion Areolar diameter was revealed as being a significant factor influencing subjective long-term satisfaction in breast asymmetry patients. Moreover, 3D volumetry proves to be an effective tool to substantiate subjective patient assessments. Our findings may lead to further improvements to surgical planning and will be expanded in further studies. KW - Congenital breast asymmetry KW - Poland syndrome KW - Lipofilling KW - Silicone implant KW - 3D volumetry Y1 - 2021 U6 - https://doi.org/10.1007/s00404-021-06218-0 PB - Springer Nature ER - TY - CHAP A1 - Franz, Daniela A1 - Dreher, Maria A1 - Prinzen, Martin A1 - Teßmann, Matthias A1 - Palm, Christoph A1 - Katzky, Uwe A1 - Perret, Jerome A1 - Hofer, Mathias A1 - Wittenberg, Thomas T1 - CT-basiertes virtuelles Fräsen am Felsenbein BT - Bild- und haptischen Wiederholfrequenzen bei unterschiedlichen Rendering Methoden T2 - Bildverarbeitung für die Medizin 2018; Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 11. bis 13. März 2018 in Erlangen N2 - Im Rahmen der Entwicklung eines haptisch-visuellen Trainingssystems für das Fräsen am Felsenbein werden ein Haptikarm und ein autostereoskopischer 3D-Monitor genutzt, um Chirurgen die virtuelle Manipulation von knöchernen Strukturen im Kontext eines sog. Serious Game zu ermöglichen. Unter anderem sollen Assistenzärzte im Rahmen ihrer Ausbildung das Fräsen am Felsenbein für das chirurgische Einsetzen eines Cochlea-Implantats üben können. Die Visualisierung des virtuellen Fräsens muss dafür in Echtzeit und möglichst realistisch modelliert, implementiert und evaluiert werden. Wir verwenden verschiedene Raycasting Methoden mit linearer und Nearest Neighbor Interpolation und vergleichen die visuelle Qualität und die Bildwiederholfrequenzen der Methoden. Alle verglichenen Verfahren sind sind echtzeitfähig, unterscheiden sich aber in ihrer visuellen Qualität. KW - Felsenbein KW - Fräsen KW - Virtualisierung KW - Computertomographie KW - Computerassistierte Chirurgie Y1 - 2018 SN - 978-3-662-56537-7 U6 - https://doi.org/10.1007/978-3-662-56537-7_51 SP - 176 EP - 181 PB - Springer CY - Berlin ER - TY - CHAP A1 - Maier, Johannes A1 - Huber, Michaela A1 - Katzky, Uwe A1 - Perret, Jerome A1 - Wittenberg, Thomas A1 - Palm, Christoph T1 - Force-Feedback-assisted Bone Drilling Simulation Based on CT Data T2 - Bildverarbeitung für die Medizin 2018; Algorithmen - Systeme - Anwendungen; Proceedings des Workshops vom 11. bis 13. März 2018 in Erlangen N2 - In order to fix a fracture using minimally invasive surgery approaches, surgeons are drilling complex and tiny bones with a 2 dimensional X-ray as single imaging modality in the operating room. Our novel haptic force-feedback and visual assisted training system will potentially help hand surgeons to learn the drilling procedure in a realistic visual environment. Within the simulation, the collision detection as well as the interaction between virtual drill, bone voxels and surfaces are important. In this work, the chai3d collision detection and force calculation algorithms are combined with a physics engine to simulate the bone drilling process. The chosen Bullet-Physics-Engine provides a stable simulation of rigid bodies, if the collision model of the drill and the tool holder is generated as a compound shape. Three haptic points are added to the K-wire tip for removing single voxels from the bone. For the drilling process three modes are proposed to emulate the different phases of drilling in restricting the movement of a haptic device. KW - Handchirurgie KW - Osteosynthese KW - Simulation KW - Lernprogramm Y1 - 2018 U6 - https://doi.org/10.1007/978-3-662-56537-7_78 SP - 291 EP - 296 PB - Springer CY - Berlin ER - TY - CHAP A1 - Eixelberger, Thomas A1 - Wittenberg, Thomas A1 - Perret, Jerome A1 - Katzky, Uwe A1 - Simon, Martina A1 - Schmitt-Rüth, Stephanie A1 - Hofer, Mathias A1 - Sorge, M. A1 - Jacob, R. A1 - Engel, Felix B. A1 - Gostian, A. A1 - Palm, Christoph A1 - Franz, Daniela T1 - A haptic model for virtual petrosal bone milling T2 - 17. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie (CURAC2018), Tagungsband, 2018, Leipzig, 13.-15. September N2 - Virtual training of bone milling requires realtime and realistic haptics of the interaction between the ”virtual mill” and a ”virtual bone”. We propose an exponential abrasion model between a virtual one and the mill bit and combine it with a coarse representation of the virtual bone and the mill shaft for collision detection using the Bullet Physics Engine. We compare our exponential abrasion model to a widely used linear abrasion model and evaluate it quantitatively and qualitatively. The evaluation results show, that we can provide virtual milling in real-time, with an abrasion behavior similar to that proposed in the literature and with a realistic feeling of five different surgeons. KW - Osteosynthese KW - Simulation KW - Lernprogramm Y1 - 2018 UR - https://www.curac.org/images/advportfoliopro/images/CURAC2018/CURAC 2018 Tagungsband.pdf VL - 17 SP - 214 EP - 219 ER - TY - CHAP A1 - Palm, Christoph A1 - Schanze, Thomas T1 - Biomedical Image and Signal Computing (BISC 2013) T2 - 58. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS 2013), Lübeck, 01.-05.09.2013 Y1 - 2013 U6 - https://doi.org/doi:10.3205/13gmds257 N1 - Meeting Abstract IS - DocAbstr. 324 PB - German Medical Science GMS Publishing House CY - Düsseldorf ER - TY - JOUR A1 - Ebigbo, Alanna A1 - Mendel, Robert A1 - Probst, Andreas A1 - Manzeneder, Johannes A1 - Souza Jr., Luis Antonio de A1 - Papa, João Paulo A1 - Palm, Christoph A1 - Messmann, Helmut T1 - Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma JF - GuT N2 - Computer-aided diagnosis using deep learning (CAD-DL) may be an instrument to improve endoscopic assessment of Barrett’s oesophagus (BE) and early oesophageal adenocarcinoma (EAC). Based on still images from two databases, the diagnosis of EAC by CAD-DL reached sensitivities/specificities of 97%/88% (Augsburg data) and 92%/100% (Medical Image Computing and Computer-Assisted Intervention [MICCAI] data) for white light (WL) images and 94%/80% for narrow band images (NBI) (Augsburg data), respectively. Tumour margins delineated by experts into images were detected satisfactorily with a Dice coefficient (D) of 0.72. This could be a first step towards CAD-DL for BE assessment. If developed further, it could become a useful adjunctive tool for patient management. KW - Speiseröhrenkrebs KW - Diagnose KW - Computerunterstütztes Verfahren KW - Maschinelles Lernen Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-68 N1 - Corresponding authors: Alanna Ebigbo and Christoph Palm VL - 68 IS - 7 SP - 1143 EP - 1145 PB - British Society of Gastroenterology ER - TY - THES A1 - Maier, Johannes T1 - Entwicklung eines Haptisch und Visuell unterstützten Trainingssystems (HaptiVisT) für komplexe Knochenbohrungen in der minimalinvasiven Handchirurgie N2 - Eine gängige Operationsmethode, um Frakturen der menschlichen Hand nach einem Unfall zu korrigieren, ist eine Osteosynthese mit sogenannten Kirschnerdrähten (K-Drähten) zur Stabilisierung von Knochenfragmenten. Die Einführung dieser langen, dünnen und scharfen Drähte durch manuelles Bohren ist eine komplexe minimalinvasive Operation, bei der ein Chirurg nahezu ohne visuelle Orientierung und nur durch eine kleine Öffnung der Haut des Patienten arbeitet. Als Orientierungshilfe für die optimale Lage der K-Drähte bleibt dem Chirurgen lediglich eine zweidimensionale (2D)-Röntgendarstellung und das Ertasten von knöchernen Vorsprüngen auf der menschlichen Hand, um Verletzungen an Risikostrukturen (Nerven, Gefäße usw.), die im Weichteilgewebe der Hand eingebettet sind, zu vermeiden. Für eine sichere und fehlerfreie Durchführung einer K-Draht-Osteosynthese ist deswegen eine gründliche theoretische und praktische Ausbildung junger Chirurgen notwendig. Da traditionelle Trainingsmethoden zeitaufwendig, kostspielig, ethisch nicht korrekt und unzureichend realistisch sind, wird in dieser Arbeit ein innovativer, auf virtueller Realität (VR) basierender, Haptisch und Visuell unterstützter Trainingssimulator (HaptiVisT) für die Platzierung von K-Drähten entwickelt, der vor allem Handchirurgen mit Übungs- und Perfektionierungsbedarf dabei unterstützt, das Bohrverfahren in einer realistischen aber virtuellen Umgebung zu erlernen. Beim HaptiVisT-Prototypenaufbau werden reale Patientendaten segmentierter Volumendaten aus einer Computertomographie (CT) und einer Magnetresonanztomographie (MRT) im virtuellen, dreidimensionalen (3D) Raum auf einem 3D-Monitor visualisiert und für eine intuitive bimanuelle Haptik sowohl mit einem Kraftfeedback-Gerät für den Bohrprozess und einer 3D-gedruckten und optisch getrackten Phantomhand kombiniert. Die vorliegende Arbeit beschreibt zunächst alle verwendeten Hardwaregeräte, die C++-Softwareumgebung, aufgebaut auf Multithreading (gleichzeitige Ausführung mehrerer Anweisungsfolgen in einem Prozess), und die auf Oberflächen- und Volumenrendering basierte Visualisierung. Die Kollisionsdetektion zwischen Bohrer und Knochen im virtuellen Raum wird in zwei separate Ereignisse unterteilt: Kollisionen zwischen Objekten als Gesamtes (Simulation der Kollision über die gesamte Objektoberfläche) und Kollisionen zwischen einer K-Draht-Spitze und dem Knochenvolumen für die Entfernung kleiner Volumenelemente (Voxel). Das Herzstück des Trainingssystems bildet eine echtzeitfähige Bohrsimulation, die den gesamten Bohrprozess in eine endliche Anzahl logischer Unterprozesse gliedert und diese Zustände in einen endlichen Zustandsautomaten (FSM, engl.: Finite State Machine) zusammenfasst. Das Kraftfeedback während einer Bohrung wird mit sogenannten „Virtual Fixtures“ (abstrakten sensorischen Informationen) berechnet und über einen Haptikarm auf den Benutzer übertragen. Damit die Simulation der Realität entspricht, wird unter Zuhilfenahme eines experimentellen Aufbaus die reale Bohrgeschwindigkeit durch kortikale Knochen ermittelt. Anschließend werden ein Levelkonzept und alle im System verfügbaren Bohrunterstützungswerkzeuge, wie haptische Korridore als Goldstandard oder eine Röntgenbildsimulation, vorgestellt. Mit ihnen ist es möglich, ausgesuchte Operationsfälle in Level unterschiedlicher Schwierigkeit zu unterteilen und den Operationsvorgang qualitativ zu bewerten. Der 3D-Druck einer Phantomhand (realitätsnahe Nachbildung einer Patientenhand) mit realistischen haptischen Eigenschaften zum Ertasten von Knochenvorsprüngen wird über einen metamaterialbasierten Ansatz (Neuanordnung des Grundmaterials durch eine künstlich angelegte, sich wiederholende Struktur) realisiert, da das aktuell am Markt verfügbare 3D-Druckmaterial für den Druck menschlichen Weichteilgewebes zu hart ist. Die Echtzeitverfolgung der Phantomhand beruht auf einem mit einer Stereokamera optisch getrackten Marker in Form eines Dodekaeders (Körper mit zwölf Flächen). Abschließend wird das HaptiVisT-Gesamtsystem in drei und der 3D-Druck einer Phantomhand in zwei Expertenevaluationen ausführlich untersucht und ausgewertet. Das HaptiVisT-System versteht sich als notwendiges Komplement für den ersten und weltweit einzigen funktionsfähigen, kompakten Prototypen für virtuelle K-DrahtOsteosynthesen mit haptischen Kraftfeedback, der in Zukunft Chirurgen in Aus- und Weiterbildung an Kliniken oder Trainingszentren ein risikofreies, zeit- und ortsunabhängiges Training ermöglicht. Die Kernelemente dieser Arbeit sind: • Stereoskopische 3D-Darstellung von realen Patientendaten. • Bimanuelle Haptik aus haptischen Kraftfeedback des Bohrens verbunden mit einer optisch getrackten, haptisch korrekten und 3D-gedruckten Phantomhand. • Zuverlässige Kollisionsdetektion zwischen virtuellen Objekten als Grundlage für das Kraftfeedback und die Abtragung von Knochen. • Echtzeitfähige Bohrsimulation durch Reduzierung des Bohrprozesses auf logische Bohr-Teilprozesse kombiniert mit einer Virtual Fixtures basierten Kraftberechnung. Haptisch korrekte Phantome sind vor allem im medizinischen Training von hoher Relevanz und die Berechnung des Kraftfeedbacks beruht erstmals auf der performanten und stabilen Simulation von Bohr-Teilprozessen unter Verwendung von Virtual Fixtures. Der Prototyp wird von Experten durchgehend positiv bewertet und bietet nach deren Einschätzung einen hohen Mehrwert für das chirurgische Training. Zukünftige Arbeiten könnten den Lerneffekt durch das HaptiVisT-Trainingssystem in stichhaltigen Evaluationen mit jungen Medizinstudenten unter Vorhandensein einer Kontrollgruppe statistisch validieren. Bei Bestätigung dieses Lerneffekts ist eine Ausgründung als eigenständiges Unternehmen und Weiterentwicklung des Prototyps mit Ausweitung auf weitere chirurgische Bereiche wie Knie- oder Hüftchirurgie denkbar. Unter Zuhilfenahme von automatischer Segmentierung könnten in Zukunft akut zu behandelnde Brüche abgebildet, vorab einer tatsächlichen Operation geübt und anschließend komplikationslos unter reduzierter Operationszeit durchgeführt werden. KW - Medizinisches Trainingssystem KW - Virtual Reality KW - Minimalinvasive Handchirurgie Y1 - 2020 SN - 978-3-8440-7547-2 N1 - Titel verleihende Institution: Universität Regensburg PB - Shaker CY - Düren ER - TY - JOUR A1 - Wöhl, Rebecca A1 - Maier, Johannes A1 - Gehmert, Sebastian A1 - Palm, Christoph A1 - Riebschläger, Birgit A1 - Nerlich, Michael A1 - Huber, Michaela T1 - 3D Analysis of Osteosyntheses Material using semi-automated CT Segmentation BT - a case series of a 4 corner fusion plate JF - BMC Musculoskeletal Disorders N2 - Backround Scaphoidectomy and midcarpal fusion can be performed using traditional fixation methods like K-wires, staples, screws or different dorsal (non)locking arthrodesis systems. The aim of this study is to test the Aptus four corner locking plate and to compare the clinical findings to the data revealed by CT scans and semi-automated segmentation. Methods: This is a retrospective review of eleven patients suffering from scapholunate advanced collapse (SLAC) or scaphoid non-union advanced collapse (SNAC) wrist, who received a four corner fusion between August 2011 and July 2014. The clinical evaluation consisted of measuring the range of motion (ROM), strength and pain on a visual analogue scale (VAS). Additionally, the Disabilities of the Arm, Shoulder and Hand (QuickDASH) and the Mayo Wrist Score were assessed. A computerized tomography (CT) of the wrist was obtained six weeks postoperatively. After semi-automated segmentation of the CT scans, the models were post processed and surveyed. Results During the six-month follow-up mean range of motion (ROM) of the operated wrist was 60°, consisting of 30° extension and 30° flexion. While pain levels decreased significantly, 54% of grip strength and 89% of pinch strength were preserved compared to the contralateral healthy wrist. Union could be detected in all CT scans of the wrist. While X-ray pictures obtained postoperatively revealed no pathology, two user related technical complications were found through the 3D analysis, which correlated to the clinical outcome. Conclusion Due to semi-automated segmentation and 3D analysis it has been proved that the plate design can keep up to the manufacturers’ promises. Over all, this case series confirmed that the plate can compete with the coexisting techniques concerning clinical outcome, union and complication rate. KW - Handchirurgie KW - Osteosynthese KW - Arthrodese KW - 4FC KW - SLAC wrist KW - SNAC wrist KW - Semi-automated segmentation KW - 3D analysis KW - Computertomographie KW - Bildsegmentierung KW - Dreidimensionale Bildverarbeitung Y1 - 2018 U6 - https://doi.org/10.1186/s12891-018-1975-0 VL - 19 SP - 1 EP - 8 PB - Springer Nature ER - TY - GEN A1 - Scheppach, Markus W. A1 - Mendel, Robert A1 - Probst, Andreas A1 - Rauber, David A1 - Rückert, Tobias A1 - Meinikheim, Michael A1 - Palm, Christoph A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Real-time detection and delineation of tissue during third-space endoscopy using artificial intelligence (AI) T2 - Endoscopy N2 - Aims  AI has proven great potential in assisting endoscopists in diagnostics, however its role in therapeutic endoscopy remains unclear. Endoscopic submucosal dissection (ESD) is a technically demanding intervention with a slow learning curve and relevant risks like bleeding and perforation. Therefore, we aimed to develop an algorithm for the real-time detection and delineation of relevant structures during third-space endoscopy. Methods  5470 still images from 59 full length videos (47 ESD, 12 POEM) were annotated. 179681 additional unlabeled images were added to the training dataset. Consequently, a DeepLabv3+ neural network architecture was trained with the ECMT semi-supervised algorithm (under review elsewhere). Evaluation of vessel detection was performed on a dataset of 101 standardized video clips from 15 separate third-space endoscopy videos with 200 predefined blood vessels. Results  Internal validation yielded an overall mean Dice score of 85% (68% for blood vessels, 86% for submucosal layer, 88% for muscle layer). On the video test data, the overall vessel detection rate (VDR) was 94% (96% for ESD, 74% for POEM). The median overall vessel detection time (VDT) was 0.32 sec (0.3 sec for ESD, 0.62 sec for POEM). Conclusions  Evaluation of the developed algorithm on a video test dataset showed high VDR and quick VDT, especially for ESD. Further research will focus on a possible clinical benefit of the AI application for VDR and VDT during third-space endoscopy. KW - Speiseröhrenkrankheit KW - Künstliche Intelligenz KW - Artificial Intelligence Y1 - 2023 U6 - https://doi.org/10.1055/s-0043-1765128 VL - 55 IS - S02 SP - S53 EP - S54 PB - Thieme ER - TY - CHAP A1 - Franz, Daniela A1 - Katzky, Uwe A1 - Neumann, Sabine A1 - Perret, Jerome A1 - Hofer, Mathias A1 - Huber, Michaela A1 - Schmitt-Rüth, Stephanie A1 - Haug, Sonja A1 - Weber, Karsten A1 - Prinzen, Martin A1 - Palm, Christoph A1 - Wittenberg, Thomas T1 - Haptisches Lernen für Cochlea Implantationen BT - Konzept - HaptiVisT Projekt T2 - 15. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie (CURAC2016), Tagungsband, 2016, Bern, 29.09. - 01.10. N2 - Die Implantation eines Cochlea Implantates benötigt einen chirurgischen Zugang im Felsenbein und durch die Paukenhöhle des Patienten. Der Chirurg hat eine eingeschränkte Sicht im Operationsgebiet, die weiterhin viele Risikostrukturen enthält. Um eine Cochlea Implantation sicher und fehlerfrei durchzuführen, ist eine umfangreiche theoretische und praktische (teilweise berufsbegleitende) Fortbildung sowie langjährige Erfahrung notwendig. Unter Nutzung von realen klinischen CT/MRT Daten von Innen- und Mittelohr und der interaktiven Segmentierung der darin abgebildeten Strukturen (Nerven, Cochlea, Gehörknöchelchen,...) wird im HaptiVisT Projekt ein haptisch-visuelles Trainingssystem für die Implantation von Innen- und Mittelohr-Implantaten realisiert, das als sog. „Serious Game“ mit immersiver Didaktik gestaltet wird. Die Evaluierung des Demonstrators hinsichtlich Zweckmäßigkeit erfolgt prozessbegleitend und ergebnisorientiert, um mögliche technische oder didaktische Fehler vor Fertigstellung des Systems aufzudecken. Drei zeitlich versetzte Evaluationen fokussieren dabei chirurgisch-fachliche, didaktische sowie haptisch-ergonomische Akzeptanzkriterien. KW - Virtuelles Training KW - Haptisches Feedback KW - Gamification in der Medizin KW - Cochlea-Implantat KW - Operationstechnik KW - Simulation KW - Haptische Feedback-Technologie KW - Lernprogramm Y1 - 2016 UR - https://curac.org/images/advportfoliopro/images/CURAC2016/CURAC%202016%20Tagungsband.pdf SP - 21 EP - 26 ER - TY - CHAP A1 - Souza Jr., Luis Antonio de A1 - Hook, Christian A1 - Papa, João Paulo A1 - Palm, Christoph T1 - Barrett's Esophagus Analysis Using SURF Features T2 - Bildverarbeitung für die Medizin 2017; Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 12. bis 14. März 2017 in Heidelberg N2 - The development of adenocarcinoma in Barrett’s esophagus is difficult to detect by endoscopic surveillance of patients with signs of dysplasia. Computer assisted diagnosis of endoscopic images (CAD) could therefore be most helpful in the demarcation and classification of neoplastic lesions. In this study we tested the feasibility of a CAD method based on Speeded up Robust Feature Detection (SURF). A given database containing 100 images from 39 patients served as benchmark for feature based classification models. Half of the images had previously been diagnosed by five clinical experts as being ”cancerous”, the other half as ”non-cancerous”. Cancerous image regions had been visibly delineated (masked) by the clinicians. SURF features acquired from full images as well as from masked areas were utilized for the supervised training and testing of an SVM classifier. The predictive accuracy of the developed CAD system is illustrated by sensitivity and specificity values. The results based on full image matching where 0.78 (sensitivity) and 0.82 (specificity) were achieved, while the masked region approach generated results of 0.90 and 0.95, respectively. KW - Speiseröhrenkrankheit KW - Diagnose KW - Maschinelles Sehen KW - Automatische Klassifikation Y1 - 2017 U6 - https://doi.org/10.1007/978-3-662-54345-0_34 SP - 141 EP - 146 PB - Springer CY - Berlin ER - TY - CHAP A1 - Mendel, Robert A1 - Ebigbo, Alanna A1 - Probst, Andreas A1 - Messmann, Helmut A1 - Palm, Christoph T1 - Barrett’s Esophagus Analysis Using Convolutional Neural Networks T2 - Bildverarbeitung für die Medizin 2017; Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 12. bis 14. März 2017 in Heidelberg N2 - We propose an automatic approach for early detection of adenocarcinoma in the esophagus. High-definition endoscopic images (50 cancer, 50 Barrett) are partitioned into a dataset containing approximately equal amounts of patches showing cancerous and non-cancerous regions. A deep convolutional neural network is adapted to the data using a transfer learning approach. The final classification of an image is determined by at least one patch, for which the probability being a cancer patch exceeds a given threshold. The model was evaluated with leave one patient out cross-validation. With sensitivity and specificity of 0.94 and 0.88, respectively, our findings improve recently published results on the same image data base considerably. Furthermore, the visualization of the class probabilities of each individual patch indicates, that our approach might be extensible to the segmentation domain. KW - Speiseröhrenkrebs KW - Diagnose KW - Maschinelles Lernen KW - Bilderkennung KW - Automatische Klassifikation Y1 - 2017 U6 - https://doi.org/10.1007/978-3-662-54345-0_23 SP - 80 EP - 85 PB - Springer CY - Berlin ER - TY - CHAP A1 - Maier, Johannes A1 - Haug, Sonja A1 - Huber, Michaela A1 - Katzky, Uwe A1 - Neumann, Sabine A1 - Perret, Jérôme A1 - Prinzen, Martin A1 - Weber, Karsten A1 - Wittenberg, Thomas A1 - Wöhl, Rebecca A1 - Scorna, Ulrike A1 - Palm, Christoph T1 - Development of a haptic and visual assisted training simulation concept for complex bone drilling in minimally invasive hand surgery T2 - CARS Conference, 5.10.-7.10.2017 Y1 - 2017 ER - TY - JOUR A1 - Souza Jr., Luis Antonio de A1 - Passos, Leandro A. A1 - Santana, Marcos Cleison S. A1 - Mendel, Robert A1 - Rauber, David A1 - Ebigbo, Alanna A1 - Probst, Andreas A1 - Messmann, Helmut A1 - Papa, João Paulo A1 - Palm, Christoph T1 - Layer-selective deep representation to improve esophageal cancer classification JF - Medical & Biological Engineering & Computing N2 - Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their accountability and transparency level must be improved to transfer this success into clinical practice. The reliability of machine learning decisions must be explained and interpreted, especially for supporting the medical diagnosis.For this task, the deep learning techniques’ black-box nature must somehow be lightened up to clarify its promising results. Hence, we aim to investigate the impact of the ResNet-50 deep convolutional design for Barrett’s esophagus and adenocarcinoma classification. For such a task, and aiming at proposing a two-step learning technique, the output of each convolutional layer that composes the ResNet-50 architecture was trained and classified for further definition of layers that would provide more impact in the architecture. We showed that local information and high-dimensional features are essential to improve the classification for our task. Besides, we observed a significant improvement when the most discriminative layers expressed more impact in the training and classification of ResNet-50 for Barrett’s esophagus and adenocarcinoma classification, demonstrating that both human knowledge and computational processing may influence the correct learning of such a problem. KW - Multistep training KW - Barrett’s esophagus detection KW - Convolutional neural networks KW - Deep learning Y1 - 2024 U6 - https://doi.org/10.1007/s11517-024-03142-8 VL - 62 SP - 3355 EP - 3372 PB - Springer Nature CY - Heidelberg ER - TY - JOUR A1 - Ebigbo, Alanna A1 - Mendel, Robert A1 - Probst, Andreas A1 - Meinikheim, Michael A1 - Byrne, Michael F. A1 - Messmann, Helmut A1 - Palm, Christoph T1 - Multimodal imaging for detection and segmentation of Barrett’s esophagus-related neoplasia using artificial intelligence JF - Endoscopy N2 - The early diagnosis of cancer in Barrett’s esophagus is crucial for improving the prognosis. However, identifying Barrett’s esophagus-related neoplasia (BERN) is challenging, even for experts [1]. Four-quadrant biopsies may improve the detection of neoplasia, but they can be associated with sampling errors. The application of artificial intelligence (AI) to the assessment of Barrett’s esophagus could improve the diagnosis of BERN, and this has been demonstrated in both preclinical and clinical studies [2] [3]. In this video demonstration, we show the accurate detection and delineation of BERN in two patients ([Video 1]). In part 1, the AI system detects a mucosal cancer about 20 mm in size and accurately delineates the lesion in both white-light and narrow-band imaging. In part 2, a small island of BERN with high-grade dysplasia is detected and delineated in white-light, narrow-band, and texture and color enhancement imaging. The video shows the results using a transparent overlay of the mucosal cancer in real time as well as a full segmentation preview. Additionally, the optical flow allows for the assessment of endoscope movement, something which is inversely related to the reliability of the AI prediction. We demonstrate that multimodal imaging can be applied to the AI-assisted detection and segmentation of even small focal lesions in real time. KW - Video KW - Artificial Intelligence KW - Multimodal Imaging Y1 - 2022 U6 - https://doi.org/10.1055/a-1704-7885 VL - 54 IS - 10 PB - Georg Thieme Verlag CY - Stuttgart ET - E-Video ER - TY - JOUR A1 - Kolev, Kalin A1 - Kirchgeßner, Norbert A1 - Houben, Sebastian A1 - Csiszár, Agnes A1 - Rubner, Wolfgang A1 - Palm, Christoph A1 - Eiben, Björn A1 - Merkel, Rudolf A1 - Cremers, Daniel T1 - A variational approach to vesicle membrane reconstruction from fluorescence imaging JF - Pattern Recognition N2 - Biological applications like vesicle membrane analysis involve the precise segmentation of 3D structures in noisy volumetric data, obtained by techniques like magnetic resonance imaging (MRI) or laser scanning microscopy (LSM). Dealing with such data is a challenging task and requires robust and accurate segmentation methods. In this article, we propose a novel energy model for 3D segmentation fusing various cues like regional intensity subdivision, edge alignment and orientation information. The uniqueness of the approach consists in the definition of a new anisotropic regularizer, which accounts for the unbalanced slicing of the measured volume data, and the generalization of an efficient numerical scheme for solving the arising minimization problem, based on linearization and fixed-point iteration. We show how the proposed energy model can be optimized globally by making use of recent continuous convex relaxation techniques. The accuracy and robustness of the presented approach are demonstrated by evaluating it on multiple real data sets and comparing it to alternative segmentation methods based on level sets. Although the proposed model is designed with focus on the particular application at hand, it is general enough to be applied to a variety of different segmentation tasks. KW - 3D segmentation KW - Convex optimization KW - Vesicle membrane analysis KW - Fluorescence imaging KW - Dreidimensionale Bildverarbeitung KW - Bildsegmentierung KW - Konvexe Optimierung Y1 - 2011 U6 - https://doi.org/10.1016/j.patcog.2011.04.019 VL - 44 IS - 12 SP - 2944 EP - 2958 PB - Elsevier ER - TY - CHAP A1 - Metzler, V. A1 - Aach, T. A1 - Palm, Christoph A1 - Lehmann, Thomas M. T1 - Texture Classification of Graylevel Images by Multiscale Cross-Co-Occurrence Matrices T2 - Proceedings 15th International Conference on Pattern Recognition (ICPR-2000) N2 - Local gray level dependencies of natural images can be modelled by means of co-occurrence matrices containing joint probabilities of gray-level pairs. Texture, however, is a resolution-dependent phenomenon and hence, classification depends on the chosen scale. Since there is no optimal scale for all textures we employ a multiscale approach that acquires textural features at several scales. Thus linear and nonlinear scale-spaces are analyzed by multiscale co-occurrence matrices that describe the statistical behavior of a texture in scale-space. Classification is then performed on the basis of texture features taken from the individual scale with the highest discriminatory power. By considering cross-scale occurrences of gray level pairs, the impact of filters on the feature is described and used for classification of natural textures. This novel method was found to improve classification rates of the common co-occurrence matrix approach on standard textures significantly. Y1 - 2000 U6 - https://doi.org/10.1109/ICPR.2000.906133 SP - 549 EP - 552 ER - TY - GEN ED - Handels, Heinz ED - Deserno, Thomas M. ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Palm, Christoph ED - Tolxdorff, Thomas T1 - Bildverarbeitung für die Medizin 2019 BT - Algorithmen – Systeme – Anwendungen. Proceedings des Workshops vom 17. bis 19. März 2019 in Lübeck N2 - In den letzten Jahren hat sich der Workshop "Bildverarbeitung für die Medizin" durch erfolgreiche Veranstaltungen etabliert. Ziel ist auch 2019 wieder die Darstellung aktueller Forschungsergebnisse und die Vertiefung der Gespräche zwischen Wissenschaftlern, Industrie und Anwendern. Die Beiträge dieses Bandes - einige davon in englischer Sprache - umfassen alle Bereiche der medizinischen Bildverarbeitung, insbesondere Bildgebung und -akquisition, Maschinelles Lernen, Bildsegmentierung und Bildanalyse, Visualisierung und Animation, Zeitreihenanalyse, Computerunterstützte Diagnose, Biomechanische Modellierung, Validierung und Qualitätssicherung, Bildverarbeitung in der Telemedizin u.v.m. KW - Bildgebendes Verfahren KW - Computerunterstützte Medizin KW - Bildanalyse KW - Bilderkennung KW - Computerunterstützte Medizin KW - Computerunterstützte Diagnose KW - Medizininformatik Y1 - 2019 SN - 978-3-658-25325-7 U6 - https://doi.org/10.1007/978-3-658-25326-4 SN - 1431-472X PB - Springer CY - Berlin ER - TY - GEN ED - Tolxdorff, Thomas ED - Deserno, Thomas M. ED - Handels, Heinz ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Palm, Christoph T1 - Bildverarbeitung für die Medizin 2020 BT - Algorithmen – Systeme – Anwendungen. Proceedings des Workshops vom 15. bis 17. März 2020 in Berlin N2 - In den letzten Jahren hat sich der Workshop "Bildverarbeitung für die Medizin" durch erfolgreiche Veranstaltungen etabliert. Ziel ist auch 2020 wieder die Darstellung aktueller Forschungsergebnisse und die Vertiefung der Gespräche zwischen Wissenschaftlern, Industrie und Anwendern. Die Beiträge dieses Bandes - einige davon in englischer Sprache - umfassen alle Bereiche der medizinischen Bildverarbeitung, insbesondere Bildgebung und -akquisition, Maschinelles Lernen, Bildsegmentierung und Bildanalyse, Visualisierung und Animation, Zeitreihenanalyse, Computerunterstützte Diagnose, Biomechanische Modellierung, Validierung und Qualitätssicherung, Bildverarbeitung in der Telemedizin u.v.m. KW - Bildanalyse KW - Bildverarbeitung KW - Computerunterstützte Medizin KW - Deep Learning KW - Visualisierung Y1 - 2020 SN - 978-3-658-29266-9 U6 - https://doi.org/10.1007/978-3-658-29267-6 PB - Springer Vieweg CY - Wiesbaden ER - TY - GEN A1 - Meinikheim, Michael A1 - Mendel, Robert A1 - Probst, Andreas A1 - Scheppach, Markus W. A1 - Messmann, Helmut A1 - Palm, Christoph A1 - Ebigbo, Alanna T1 - Optical Flow als Methode zur Qualitätssicherung KI-unterstützter Untersuchungen von Barrett-Ösophagus und Barrett-Ösophagus assoziierten Neoplasien T2 - Zeitschrift für Gastroenterologie N2 - Einleitung  Übermäßige Bewegung im Bild kann die Performance von auf künstlicher Intelligenz (KI) basierenden klinischen Entscheidungsunterstützungssystemen (CDSS) reduzieren. Optical Flow (OF) ist eine Methode zur Lokalisierung und Quantifizierung von Bewegungen zwischen aufeinanderfolgenden Bildern. Ziel  Ziel ist es, die Mensch-Computer-Interaktion (HCI) zu verbessern und Endoskopiker die unser KI-System „Barrett-Ampel“ zur Unterstützung bei der Beurteilung von Barrett-Ösophagus (BE) verwenden, ein Echtzeit-Feedback zur aktuellen Datenqualität anzubieten. Methodik  Dazu wurden unveränderte Videos in „Weißlicht“ (WL), „Narrow Band Imaging“ (NBI) und „Texture and Color Enhancement Imaging“ (TXI) von acht endoskopischen Untersuchungen von histologisch gesichertem BE und mit Barrett-Ösophagus assoziierten Neoplasien (BERN) durch unseren KI-Algorithmus analysiert. Der zur Bewertung der Bildqualität verwendete OF beinhaltete die mittlere Magnitude und die Entropie des Histogramms der Winkel. Frames wurden automatisch extrahiert, wenn die vordefinierten Schwellenwerte von 3,0 für die mittlere Magnitude und 9,0 für die Entropie des Histogramms der Winkel überschritten wurden. Experten sahen sich zunächst die Videos ohne KI-Unterstützung an und bewerteten, ob Störfaktoren die Sicherheit mit der eine Diagnose im vorliegenden Fall gestellt werden kann negativ beeinflussen. Anschließend überprüften sie die extrahierten Frames. Ergebnis  Gleichmäßige Bewegung in eine Richtung, wie etwa beim Vorschieben des Endoskops, spiegelte sich, bei insignifikant veränderter Entropie, in einer Erhöhung der Magnitude wider. Chaotische Bewegung, zum Beispiel während dem Spülen, war mit erhöhter Entropie assoziiert. Insgesamt war eine unruhige endoskopische Darstellung, Flüssigkeit sowie übermäßige Ösophagusmotilität mit erhöhtem OF assoziiert und korrelierte mit der Meinung der Experten über die Qualität der Videos. Der OF und die subjektive Wahrnehmung der Experten über die Verwertbarkeit der vorliegenden Bildsequenzen korrelierten direkt proportional. Wenn die vordefinierten Schwellenwerte des OF überschritten wurden, war die damit verbundene Bildqualität in 94% der Fälle für eine definitive Interpretation auch für Experten unzureichend. Schlussfolgerung  OF hat das Potenzial Endoskopiker ein Echtzeit-Feedback über die Qualität des Dateninputs zu bieten und so nicht nur die HCI zu verbessern, sondern auch die optimale Performance von KI-Algorithmen zu ermöglichen. KW - Optical Flow Y1 - 2022 U6 - https://doi.org/10.1055/s-0042-1754997 VL - 60 IS - 08 PB - Georg Thieme Verlag CY - Stuttgart ER - TY - GEN A1 - Meinikheim, Michael A1 - Mendel, Robert A1 - Scheppach, Markus W. A1 - Probst, Andreas A1 - Prinz, Friederike A1 - Schwamberger, Tanja A1 - Schlottmann, Jakob A1 - Gölder, Stefan Karl A1 - Walter, Benjamin A1 - Steinbrück, Ingo A1 - Palm, Christoph A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Einsatz von künstlicher Intelligenz (KI) als Entscheidungsunterstützungssystem für nicht-Experten bei der Beurteilung von Barrett-Ösophagus assoziierten Neoplasien (BERN) T2 - Zeitschrift für Gastroenterologie N2 - Einleitung Die sichere Detektion und Charakterisierung von Barrett-Ösophagus assoziierten Neoplasien (BERN) stellt selbst für erfahrene Endoskopiker eine Herausforderung dar. Ziel Ziel dieser Studie ist es, den Add-on Effekt eines künstlichen Intelligenz (KI) Systems (Barrett-Ampel) als Entscheidungsunterstüzungssystem für Endoskopiker ohne Expertise bei der Untersuchung von BERN zu evaluieren. Material und Methodik Zwölf Videos in „Weißlicht“ (WL), „narrow-band imaging“ (NBI) und „texture and color enhanced imaging“ (TXI) von histologisch bestätigten Barrett-Metaplasien oder BERN wurden von Experten und Untersuchern ohne Barrett-Expertise evaluiert. Die Probanden wurden dazu aufgefordert in den Videos auftauchende BERN zu identifizieren und gegebenenfalls die optimale Biopsiestelle zu markieren. Unser KI-System wurde demselben Test unterzogen, wobei dieses BERN in Echtzeit segmentierte und farblich von umliegendem Epithel differenzierte. Anschließend wurden den Probanden die Videos mit zusätzlicher KI-Unterstützung gezeigt. Basierend auf dieser neuen Information, wurden die Probanden zu einer Reevaluation ihrer initialen Beurteilung aufgefordert. Ergebnisse Die „Barrett-Ampel“ identifizierte unabhängig von den verwendeten Darstellungsmodi (WL, NBI, TXI) alle BERN. Zwei entzündlich veränderte Läsionen wurden fehlinterpretiert (Genauigkeit=75%). Während Experten vergleichbare Ergebnisse erzielten (Genauigkeit=70,8%), hatten Endoskopiker ohne Expertise bei der Beurteilung von Barrett-Metaplasien eine Genauigkeit von lediglich 58,3%. Wurden die nicht-Experten allerdings von unserem KI-System unterstützt, erreichten diese eine Genauigkeit von 75%. Zusammenfassung Unser KI-System hat das Potential als Entscheidungsunterstützungssystem bei der Differenzierung zwischen Barrett-Metaplasie und BERN zu fungieren und so Endoskopiker ohne entsprechende Expertise zu assistieren. Eine Limitation dieser Studie ist die niedrige Anzahl an eingeschlossenen Videos. Um die Ergebnisse dieser Studie zu bestätigen, müssen randomisierte kontrollierte klinische Studien durchgeführt werden. KW - Barrett-Ösophagus KW - Künstliche Intelligenz Y1 - 2022 U6 - https://doi.org/10.1055/s-0042-1745653 VL - 60 IS - 4 SP - 251 PB - Thieme CY - Stuttgart ER - TY - GEN A1 - Meinikheim, Michael A1 - Mendel, Robert A1 - Scheppach, Markus W. A1 - Probst, Andreas A1 - Prinz, Friederike A1 - Schwamberger, Tanja A1 - Schlottmann, Jakob A1 - Gölder, Stefan Karl A1 - Walter, Benjamin A1 - Steinbrück, Ingo A1 - Palm, Christoph A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - INFLUENCE OF AN ARTIFICIAL INTELLIGENCE (AI) BASED DECISION SUPPORT SYSTEM (DSS) ON THE DIAGNOSTIC PERFORMANCE OF NON-EXPERTS IN BARRETT´S ESOPHAGUS RELATED NEOPLASIA (BERN) T2 - Endoscopy N2 - Aims Barrett´s esophagus related neoplasia (BERN) is difficult to detect and characterize during endoscopy, even for expert endoscopists. We aimed to assess the add-on effect of an Artificial Intelligence (AI) algorithm (Barrett-Ampel) as a decision support system (DSS) for non-expert endoscopists in the evaluation of Barrett’s esophagus (BE) and BERN. Methods Twelve videos with multimodal imaging white light (WL), narrow-band imaging (NBI), texture and color enhanced imaging (TXI) of histologically confirmed BE and BERN were assessed by expert and non-expert endoscopists. For each video, endoscopists were asked to identify the area of BERN and decide on the biopsy spot. Videos were assessed by the AI algorithm and regions of BERN were highlighted in real-time by a transparent overlay. Finally, endoscopists were shown the AI videos and asked to either confirm or change their initial decision based on the AI support. Results Barrett-Ampel correctly identified all areas of BERN, irrespective of the imaging modality (WL, NBI, TXI), but misinterpreted two inflammatory lesions (Accuracy=75%). Expert endoscopists had a similar performance (Accuracy=70,8%), while non-experts had an accuracy of 58.3%. When AI was implemented as a DSS, non-expert endoscopists improved their diagnostic accuracy to 75%. Conclusions AI may have the potential to support non-expert endoscopists in the assessment of videos of BE and BERN. Limitations of this study include the low number of videos used. Randomized clinical trials in a real-life setting should be performed to confirm these results. KW - Artificial Intelligence KW - Barrett's Esophagus KW - Speiseröhrenkrankheit KW - Künstliche Intelligenz KW - Diagnose Y1 - 2022 U6 - https://doi.org/10.1055/s-00000012 VL - 54 IS - S 01 SP - S39 PB - Thieme ER - TY - JOUR A1 - Scheppach, Markus W. A1 - Mendel, Robert A1 - Probst, Andreas A1 - Meinikheim, Michael A1 - Palm, Christoph A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - ARTIFICIAL INTELLIGENCE (AI) – ASSISTED VESSEL AND TISSUE RECOGNITION IN THIRD-SPACE ENDOSCOPY JF - Endoscopy N2 - Aims Third-space endoscopy procedures such as endoscopic submucosal dissection (ESD) and peroral endoscopic myotomy (POEM) are complex interventions with elevated risk of operator-dependent adverse events, such as intra-procedural bleeding and perforation. We aimed to design an artificial intelligence clinical decision support solution (AI-CDSS, “Smart ESD”) for the detection and delineation of vessels, tissue structures, and instruments during third-space endoscopy procedures. Methods Twelve full-length third-space endoscopy videos were extracted from the Augsburg University Hospital database. 1686 frames were annotated for the following categories: Submucosal layer, blood vessels, electrosurgical knife and endoscopic instrument. A DeepLabv3+neural network with a 101-layer ResNet backbone was trained and validated internally. Finally, the ability of the AI system to detect visible vessels during ESD and POEM was determined on 24 separate video clips of 7 to 46 seconds duration and showing 33 predefined vessels. These video clips were also assessed by an expert in third-space endoscopy. Results Smart ESD showed a vessel detection rate (VDR) of 93.94%, while an average of 1.87 false positive signals were recorded per minute. VDR of the expert endoscopist was 90.1% with no false positive findings. On the internal validation data set using still images, the AI system demonstrated an Intersection over Union (IoU), mean Dice score and pixel accuracy of 63.47%, 76.18% and 86.61%, respectively. Conclusions This is the first AI-CDSS aiming to mitigate operator-dependent limitations during third-space endoscopy. Further clinical trials are underway to better understand the role of AI in such procedures. KW - Artificial Intelligence KW - Third-Space Endoscopy KW - Smart ESD Y1 - 2022 U6 - https://doi.org/10.1055/s-0042-1745037 VL - 54 IS - S01 SP - S175 PB - Thieme ER - TY - CHAP A1 - Weber Nunes, Danilo A1 - Rauber, David A1 - Palm, Christoph ED - Palm, Christoph ED - Breininger, Katharina ED - Deserno, Thomas M. ED - Handels, Heinz ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Tolxdorff, Thomas T1 - Self-supervised 3D Vision Transformer Pre-training for Robust Brain Tumor Classification T2 - Bildverarbeitung für die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Regensburg March 09-11, 2025 N2 - Brain tumors pose significant challenges in neurology, making precise classification crucial for prognosis and treatment planning. This work investigates the effectiveness of a self-supervised learning approach–masked autoencoding (MAE)–to pre-train a vision transformer (ViT) model for brain tumor classification. Our method uses non-domain specific data, leveraging the ADNI and OASIS-3 MRI datasets, which primarily focus on degenerative diseases, for pretraining. The model is subsequently fine-tuned and evaluated on the BraTS glioma and meningioma datasets, representing a novel use of these datasets for tumor classification. The pre-trained MAE ViT model achieves an average F1 score of 0.91 in a 5-fold cross-validation setting, outperforming the nnU-Net encoder trained from scratch, particularly under limited data conditions. These findings highlight the potential of self-supervised MAE in enhancing brain tumor classification accuracy, even with restricted labeled data. Y1 - 2025 U6 - https://doi.org/10.1007/978-3-658-47422-5_69 SP - 298 EP - 303 PB - Springer Vieweg CY - Wiesbaden ER - TY - CHAP A1 - Weiherer, Maximilian A1 - von Riedheim, Antonia A1 - Brébant, Vanessa A1 - Egger, Bernhard A1 - Palm, Christoph ED - Palm, Christoph ED - Breininger, Katharina ED - Deserno, Thomas M. ED - Handels, Heinz ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Tolxdorff, Thomas T1 - iRBSM: A Deep Implicit 3D Breast Shape Model T2 - Bildverarbeitung für die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Regensburg March 09-11, 2025 N2 - We present the first deep implicit 3D shape model of the female breast, building upon and improving the recently proposed Regensburg Breast Shape Model (RBSM). Compared to its PCA-based predecessor, our model employs implicit neural representations; hence, it can be trained on raw 3D breast scans and eliminates the need for computationally demanding non-rigid registration, a task that is particularly difficult for feature-less breast shapes. The resulting model, dubbed iRBSM, captures detailed surface geometry including fine structures such as nipples and belly buttons, is highly expressive, and outperforms the RBSM on different surface reconstruction tasks. Finally, leveraging the iRBSM, we present a prototype application to 3D reconstruct breast shapes from just a single image. Model and code publicly available at https://rbsm.re-mic.de/implicit. Y1 - 2025 U6 - https://doi.org/10.1007/978-3-658-47422-5_11 SP - 38 EP - 43 PB - Springer Vieweg CY - Wiesbaden ER - TY - GEN ED - Deserno, Thomas M. ED - Handels, Heinz ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Palm, Christoph ED - Tolxdorff, Thomas T1 - Bildverarbeitung für die Medizin 2023 BT - Proceedings, German Workshop on Medical Image Computing, Braunschweig, July 2-4, 2023 N2 - Seit mehr als 25 Jahren ist der Workshop "Bildverarbeitung für die Medizin" als erfolgreiche Veranstaltung etabliert. Ziel ist auch 2023 wieder die Darstellung aktueller Forschungsergebnisse und die Vertiefung der Gespräche zwischen Wissenschaftlern, Industrie und Anwendern. Die Beiträge dieses Bandes - viele davon in englischer Sprache - umfassen alle Bereiche der medizinischen Bildverarbeitung, insbesondere die Bildgebung und -akquisition, Segmentierung und Analyse, Visualisierung und Animation, computerunterstützte Diagnose sowie bildgestützte Therapieplanung und Therapie. Hierbei kommen Methoden des maschinelles Lernens, der biomechanischen Modellierung sowie der Validierung und Qualitätssicherung zum Einsatz. KW - Machine Learning KW - Medical Image Computing KW - Bildverarbeitung KW - Computerunterstützte Medizin KW - Bildgebendes Verfahren KW - Bildanalyse KW - Visualisierung KW - Deep Learning Y1 - 2023 SN - 978-3-658-41656-0 U6 - https://doi.org/10.1007/978-3-658-41657-7 SN - 1431-472X PB - Springer Vieweg CY - Wiesbaden ER - TY - CHAP A1 - Gutbrod, Max A1 - Geisler, Benedikt A1 - Rauber, David A1 - Palm, Christoph ED - Maier, Andreas ED - Deserno, Thomas M. ED - Handels, Heinz ED - Maier-Hein, Klaus H. ED - Palm, Christoph ED - Tolxdorff, Thomas T1 - Data Augmentation for Images of Chronic Foot Wounds T2 - Bildverarbeitung für die Medizin 2024: Proceedings, German Workshop on Medical Image Computing, March 10-12, 2024, Erlangen N2 - Training data for Neural Networks is often scarce in the medical domain, which often results in models that struggle to generalize and consequently showpoor performance on unseen datasets. Generally, adding augmentation methods to the training pipeline considerably enhances a model’s performance. Using the dataset of the Foot Ulcer Segmentation Challenge, we analyze two additional augmentation methods in the domain of chronic foot wounds - local warping of wound edges along with projection and blurring of shapes inside wounds. Our experiments show that improvements in the Dice similarity coefficient and Normalized Surface Distance metrics depend on a sensible selection of those augmentation methods. Y1 - 2024 U6 - https://doi.org/10.1007/978-3-658-44037-4_71 SP - 261 EP - 266 PB - Springer CY - Wiesbaden ER - TY - GEN A1 - Schroeder, Josef A. A1 - Semmelmann, Matthias A1 - Siegmund, Heiko A1 - Grafe, Claudia A1 - Evert, Matthias A1 - Palm, Christoph T1 - Improved interactive computer-assisted approach for evaluation of ultrastructural cilia abnormalities T2 - Ultrastructural Pathology KW - Zilie KW - Ultrastruktur KW - Anomalie KW - Bildverarbeitung KW - Computerunterstütztes Verfahren Y1 - 2017 U6 - https://doi.org/10.1080/01913123.2016.1270978 VL - 41 IS - 1 SP - 112 EP - 113 ER - TY - GEN ED - Maier, Andreas ED - Deserno, Thomas M. ED - Handels, Heinz ED - Maier-Hein, Klaus H. ED - Palm, Christoph ED - Tolxdorff, Thomas T1 - Bildverarbeitung für die Medizin 2018 BT - Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 11. bis 13. März 2018 in Erlangen KW - Bildanalyse KW - Bildverarbeitung KW - Computerunterstützte Medizin KW - Bildgebendes Verfahren Y1 - 2018 U6 - https://doi.org/10.1007/978-3-662-56537-7 PB - Springer CY - Berlin ER - TY - GEN ED - Palm, Christoph ED - Breininger, Katharina ED - Deserno, Thomas M. ED - Handels, Heinz ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Tolxdorff, Thomas M. T1 - Bildverarbeitung für die Medizin 2025 BT - Proceedings, German Conference on Medical Image Computing, Regensburg March 09-11, 2025 N2 - Die Konferenz "BVM - Bildverarbeitung für die Medizin" ist seit vielen Jahren als die nationale Plattform für den Austausch von Ideen und die Diskussion der neuesten Forschungsergebnisse im Bereich der Medizinischen Bildverarbeitung und der Künstlichen Intelligenz (KI) etabliert. Auch 2025 werden wir aktuelle Forschungsergebnisse vorstellen und Gespräche zwischen (jungen) Wissenschaftler*innen, Industrie und Anwender*innen vertiefen. Die Beiträge dieses Bandes – die meisten davon in englischer Sprache - umfassen alle Bereiche der medizinischen Bildverarbeitung, insbesondere die Bildgebung und -akquisition, Segmentierung und Analyse, Registrierung, Visualisierung und Animation, computerunterstützte Diagnose sowie bildgestützte Therapieplanung und Therapie. Hierbei kommen Methoden des maschinellen Lernens, der biomechanischen Modellierung sowie der Validierung und Qualitätssicherung zum Einsatz. Das Kapitel "Leveraging multiple total body segmentators and anatomy-informed post-processing for segmenting bones in Lung CTs" ist unter einer Creative Commons Attribution 4.0 International License über link.springer.com frei verfügbar (Open Access). Die Herausgebenden Prof. Palm forscht im Bereich KI für die Medizin mit einem Schwerpunkt in der Analyse endoskopischer Bilddaten zur computerunterstützten Diagnose und Therapie. Prof. Breininger entwickelt robuste Ansätze des maschinellen Lernens in verschiedenen interdisziplinären Bereichen, mit einem Schwerpunkt auf medizinischen Bilddaten. Prof. Deserno forscht in Biosignal- und Bilderzeugung und -verarbeitung, insbesondere in der videobasierten Vitaldatenmessung. Prof. Handels entwickelt problemoptimierte, lernfähige Bildverarbeitungsmethoden und integriert diese in hybride Bildverarbeitungssysteme zur Unterstützung der medizinischen Diagnostik und Therapie. Prof. Maier entwickelt Anwendungen in der medizinischen Bildverarbeitung zur Diagnoseunterstützung bis hin zur Schichtbildberechnung durch künstliche Intelligenz. Prof. Maier-Hein forscht im Bereich maschinelles Lernen und entwickelt Open-Source-Lösungen wie das Medical Imaging Interaction Toolkit (MITK), Kaapana oder das nnU-Net. Prof. em. Tolxdorff ist Experte für maschinelles Lernen, biomedizinisches Datenmanagement, Datenvisualisierung und -analyse sowie Medizinproduktentwicklung in klinischen Workflows. KW - Bildverarbeitung KW - Computerunterstützte Medizin KW - Bildgebendes Verfahren KW - Bildanalyse KW - Deep Learning Y1 - 2025 SN - 978-3-658-47421-8 U6 - https://doi.org/10.1007/978-3-658-47422-5 SN - 1431-472X PB - Springer Fachmedien Wiesbaden CY - Wiesbaden ER - TY - JOUR A1 - Hartwig, Regine A1 - Berlet, Maximilian A1 - Czempiel, Tobias A1 - Fuchtmann, Jonas A1 - Rückert, Tobias A1 - Feussner, Hubertus A1 - Wilhelm, Dirk T1 - Bildbasierte Unterstützungsmethoden für die zukünftige Anwendung in der Chirurgie JF - Die Chirurgie N2 - Hintergrund: Die Entwicklung assistiver Technologien wird in den kommenden Jahren nicht nur in der Chirurgie von zunehmender Bedeutung sein. Die Wahrnehmung der Istsituation stellt hierbei die Grundlage jeder autonomen Handlung dar. Hierfür können unterschiedliche Sensorsysteme genutzt werden, wobei videobasierte Systeme ein besonderes Potenzial aufweisen. Methode: Anhand von Literaturangaben und auf Basis eigener Forschungsarbeiten werden zentrale Aspekte bildbasierter Unterstützungssysteme für die Chirurgie dargestellt. Hierbei wird deren Potenzial, aber auch die Limitationen der Methoden erläutert. Ergebnisse: Eine etablierte Anwendung stellt die Phasendetektion chirurgischer Eingriffe dar, für die Operationsvideos mittels neuronaler Netzwerke analysiert werden. Durch eine zeitlich gestützte und transformative Analyse konnten die Ergebnisse der Prädiktion jüngst deutlich verbessert werden. Aber auch robotische Kameraführungssysteme nutzen Bilddaten, um das Laparoskop zukünftig autonom zu navigieren. Um die Zuverlässigkeit an die hohen Anforderungen in der Chirurgie anzugleichen, müssen diese jedoch durch zusätzliche Informationen ergänzt werden. Ein vergleichbarer multimodaler Ansatz wurde bereits für die Navigation und Lokalisation bei laparoskopischen Eingriffen umgesetzt. Hierzu werden Videodaten mittels verschiedener Methoden analysiert und diese Ergebnisse mit anderen Sensormodalitäten fusioniert. Diskussion: Bildbasierte Unterstützungsmethoden sind bereits für diverse Aufgaben verfügbar und stellen einen wichtigen Aspekt für die Chirurgie der Zukunft dar. Um hier jedoch zuverlässig und für autonome Funktionen eingesetzt werden zu können, müssen sie zukünftig in multimodale Ansätze eingebettet werden, um die erforderliche Sicherheit bieten zu können. T2 - Image-based supportive measures for future application in surgery KW - Künstliche Intelligenz KW - Robotik KW - Kognitiver Operationsaal KW - Autonomie KW - Digitalisierung Y1 - 2022 U6 - https://doi.org/10.1007/s00104-022-01668-x VL - 93 SP - 956 EP - 965 PB - Springer ER - TY - CHAP A1 - Rückert, Tobias A1 - Rieder, Maximilian A1 - Feussner, Hubertus A1 - Wilhelm, Dirk A1 - Rückert, Daniel A1 - Palm, Christoph ED - Maier, Andreas ED - Deserno, Thomas M. ED - Handels, Heinz ED - Maier-Hein, Klaus H. ED - Palm, Christoph ED - Tolxdorff, Thomas T1 - Smoke Classification in Laparoscopic Cholecystectomy Videos Incorporating Spatio-temporal Information T2 - Bildverarbeitung für die Medizin 2024: Proceedings, German Workshop on Medical Image Computing, March 10-12, 2024, Erlangen N2 - Heavy smoke development represents an important challenge for operating physicians during laparoscopic procedures and can potentially affect the success of an intervention due to reduced visibility and orientation. Reliable and accurate recognition of smoke is therefore a prerequisite for the use of downstream systems such as automated smoke evacuation systems. Current approaches distinguish between non-smoked and smoked frames but often ignore the temporal context inherent in endoscopic video data. In this work, we therefore present a method that utilizes the pixel-wise displacement from randomly sampled images to the preceding frames determined using the optical flow algorithm by providing the transformed magnitude of the displacement as an additional input to the network. Further, we incorporate the temporal context at evaluation time by applying an exponential moving average on the estimated class probabilities of the model output to obtain more stable and robust results over time. We evaluate our method on two convolutional-based and one state-of-the-art transformer architecture and show improvements in the classification results over a baseline approach, regardless of the network used. Y1 - 2024 U6 - https://doi.org/10.1007/978-3-658-44037-4_78 SP - 298 EP - 303 PB - Springeer CY - Wiesbaden ER - TY - INPR A1 - Mendel, Robert A1 - Rückert, Tobias A1 - Wilhelm, Dirk A1 - Rückert, Daniel A1 - Palm, Christoph T1 - Motion-Corrected Moving Average: Including Post-Hoc Temporal Information for Improved Video Segmentation N2 - Real-time computational speed and a high degree of precision are requirements for computer-assisted interventions. Applying a segmentation network to a medical video processing task can introduce significant inter-frame prediction noise. Existing approaches can reduce inconsistencies by including temporal information but often impose requirements on the architecture or dataset. This paper proposes a method to include temporal information in any segmentation model and, thus, a technique to improve video segmentation performance without alterations during training or additional labeling. With Motion-Corrected Moving Average, we refine the exponential moving average between the current and previous predictions. Using optical flow to estimate the movement between consecutive frames, we can shift the prior term in the moving-average calculation to align with the geometry of the current frame. The optical flow calculation does not require the output of the model and can therefore be performed in parallel, leading to no significant runtime penalty for our approach. We evaluate our approach on two publicly available segmentation datasets and two proprietary endoscopic datasets and show improvements over a baseline approach. KW - Deep Learning KW - Video KW - Segmentation Y1 - 2024 U6 - https://doi.org/10.48550/arXiv.2403.03120 ER - TY - GEN A1 - Scheppach, Markus W. A1 - Mendel, Robert A1 - Probst, Andreas A1 - Meinikheim, Michael A1 - Palm, Christoph A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Artificial Intelligence (AI) – assisted vessel and tissue recognition during third space endoscopy (Smart ESD) T2 - Zeitschrift für Gastroenterologie N2 - Clinical setting  Third space procedures such as endoscopic submucosal dissection (ESD) and peroral endoscopic myotomy (POEM) are complex minimally invasive techniques with an elevated risk for operator-dependent adverse events such as bleeding and perforation. This risk arises from accidental dissection into the muscle layer or through submucosal blood vessels as the submucosal cutting plane within the expanding resection site is not always apparent. Deep learning algorithms have shown considerable potential for the detection and characterization of gastrointestinal lesions. So-called AI – clinical decision support solutions (AI-CDSS) are commercially available for polyp detection during colonoscopy. Until now, these computer programs have concentrated on diagnostics whereas an AI-CDSS for interventional endoscopy has not yet been introduced. We aimed to develop an AI-CDSS („Smart ESD“) for real-time intra-procedural detection and delineation of blood vessels, tissue structures and endoscopic instruments during third-space endoscopic procedures. Characteristics of Smart ESD  An AI-CDSS was invented that delineates blood vessels, tissue structures and endoscopic instruments during third-space endoscopy in real-time. The output can be displayed by an overlay over the endoscopic image with different modes of visualization, such as a color-coded semitransparent area overlay, or border tracing (demonstration video). Hereby the optimal layer for dissection can be visualized, which is close above or directly at the muscle layer, depending on the applied technique (ESD or POEM). Furthermore, relevant blood vessels (thickness> 1mm) are delineated. Spatial proximity between the electrosurgical knife and a blood vessel triggers a warning signal. By this guidance system, inadvertent dissection through blood vessels could be averted. Technical specifications  A DeepLabv3+ neural network architecture with KSAC and a 101-layer ResNeSt backbone was used for the development of Smart ESD. It was trained and validated with 2565 annotated still images from 27 full length third-space endoscopic videos. The annotation classes were blood vessel, submucosal layer, muscle layer, electrosurgical knife and endoscopic instrument shaft. A test on a separate data set yielded an intersection over union (IoU) of 68%, a Dice Score of 80% and a pixel accuracy of 87%, demonstrating a high overlap between expert and AI segmentation. Further experiments on standardized video clips showed a mean vessel detection rate (VDR) of 85% with values of 92%, 70% and 95% for POEM, rectal ESD and esophageal ESD respectively. False positive measurements occurred 0.75 times per minute. 7 out of 9 vessels which caused intraprocedural bleeding were caught by the algorithm, as well as both vessels which required hemostasis via hemostatic forceps. Future perspectives  Smart ESD performed well for vessel and tissue detection and delineation on still images, as well as on video clips. During a live demonstration in the endoscopy suite, clinical applicability of the innovation was examined. The lag time for processing of the live endoscopic image was too short to be visually detectable for the interventionist. Even though the algorithm could not be applied during actual dissection by the interventionist, Smart ESD appeared readily deployable during visual assessment by ESD experts. Therefore, we plan to conduct a clinical trial in order to obtain CE-certification of the algorithm. This new technology may improve procedural safety and speed, as well as training of modern minimally invasive endoscopic resection techniques. KW - Artificial Intelligence KW - Medical Image Computing KW - Endoscopy KW - Bildgebendes Verfahren KW - Medizin KW - Künstliche Intelligenz KW - Endoskopie Y1 - 2022 U6 - https://doi.org/10.1055/s-0042-1755110 VL - 60 IS - 08 PB - Georg Thieme Verlag CY - Stuttgart ER - TY - GEN A1 - Meinikheim, Michael A1 - Mendel, Robert A1 - Probst, Andreas A1 - Scheppach, Markus W. A1 - Messmann, Helmut A1 - Palm, Christoph A1 - Ebigbo, Alanna T1 - Barrett-Ampel T2 - Zeitschrift für Gastroenterologie N2 - Hintergrund  Adenokarzinome des Ösophagus sind bis heute mit einer infausten Prognose vergesellschaftet (1). Obwohl Endoskopiker mit Barrett-Ösophagus als Präkanzerose konfrontiert werden, ist vor allem für nicht-Experten die Differenzierung zwischen Barrett-Ösophagus ohne Dysplasie und assoziierten Neoplasien mitunter schwierig. Existierende Biopsieprotokolle (z.B. Seattle Protokoll) sind oftmals unzuverlässig (2). Eine frühzeitige Diagnose des Adenokarzinoms ist allerdings von fundamentaler Bedeutung für die Prognose des Patienten. Forschungsansatz  Auf der Grundlage dieser Problematik, entwickelten wir in Kooperation mit dem Forschungslabor „Regensburg Medical Image Computing (ReMIC)“ der OTH Regensburg ein auf künstlicher Intelligenz (KI) basiertes Entscheidungsunterstützungssystem (CDSS). Das auf einer DeepLabv3+ neuronalen Netzwerkarchitektur basierende CDSS differenziert mittels Mustererkennung Barrett- Ösophagus ohne Dysplasie von Barrett-Ösophagus mit Dysplasie bzw. Neoplasie („Klassifizierung“). Hierbei werden gemittelte Ausgabewahrscheinlichkeiten mit einem vom Benutzer definierten Schwellenwert verglichen. Für Vorhersagen, die den Schwellenwert überschreiten, berechnen wir die Kontur der Region und die Fläche. Sobald die vorhergesagte Läsion eine bestimmte Größe in der Eingabe überschreitet, heben wir sie und ihren Umriss hervor. So ermöglicht eine farbkodierte Visualisierung eine Abgrenzung zwischen Dysplasie bzw. Neoplasie und normalem Barrett-Epithel („Segmentierung“). In einer Studie an Bildern in „Weißlicht“ (WL) und „Narrow Band Imaging“ (NBI) demonstrierten wir eine Sensitivität von mehr als 90% und eine Spezifität von mehr als 80% (3). In einem nächsten Schritt, differenzierte unser KI-Algorithmus Barrett- Metaplasien von assoziierten Neoplasien anhand von zufällig abgegriffenen Bildern in Echtzeit mit einer Accuracy von 89.9% (4). Darauf folgend, entwickelten wir unser System dahingehend weiter, dass unser Algorithmus nun auch dazu in der Lage ist, Untersuchungsvideos in WL, NBI und „Texture and Color Enhancement Imaging“ (TXI) in Echtzeit zu analysieren (5). Aktuell führen wir eine Studie in einem randomisiert-kontrollierten Ansatz an unveränderten Untersuchungsvideos in WL, NBI und TXI durch. Ausblick  Um Patienten mit aus Barrett-Metaplasien resultierenden Neoplasien frühestmöglich an „High-Volume“-Zentren überweisen zu können, soll unser KI-Algorithmus zukünftig vor allem Endoskopiker ohne extensive Erfahrung bei der Beurteilung von Barrett- Ösophagus in der Krebsfrüherkennung unterstützen. KW - Barrett-Ösophagus KW - Adenokarzinom KW - Künstliche Intelligenz KW - Speiseröhrenkrebs KW - Diagnose KW - Künstliche Intelligenz Y1 - 2022 U6 - https://doi.org/10.1055/s-0042-1755109 VL - 60 IS - 08 PB - Georg Thieme Verlag CY - Stuttgart ER - TY - GEN A1 - Scheppach, Markus W. A1 - Mendel, Robert A1 - Probst, Andreas A1 - Meinikheim, Michael A1 - Palm, Christoph A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Intraprozedurale Strukturerkennung bei Third-Space Endoskopie mithilfe eines Deep-Learning Algorithmus T2 - Zeitschrift für Gastroenterologie N2 - Einleitung Third-Space Interventionen wie die endoskopische Submukosadissektion (ESD) und die perorale endoskopische Myotomie (POEM) sind technisch anspruchsvoll und mit einem erhöhten Risiko für intraprozedurale Komplikationen wie Blutung oder Perforation assoziiert. Moderne Computerprogramme zur Unterstützung bei diagnostischen Entscheidungen werden unter Einsatz von künstlicher Intelligenz (KI) in der Endoskopie bereits erfolgreich eingesetzt. Ziel der vorliegenden Arbeit war es, relevante anatomische Strukturen mithilfe eines Deep-Learning Algorithmus zu detektieren und segmentieren, um die Sicherheit und Anwendbarkeit von ESD und POEM zu erhöhen. Methoden Zwölf Videoaufnahmen in voller Länge von Third-Space Endoskopien wurden aus der Datenbank des Universitätsklinikums Augsburg extrahiert. 1686 Einzelbilder wurden für die Kategorien Submukosa, Blutgefäß, Dissektionsmesser und endoskopisches Instrument annotiert und segmentiert. Mit diesem Datensatz wurde ein DeepLabv3+neuronales Netzwerk auf der Basis eines ResNet mit 101 Schichten trainiert und intern anhand der Parameter Intersection over Union (IoU), Dice Score und Pixel Accuracy validiert. Die Fähigkeit des Algorithmus zur Gefäßdetektion wurde anhand von 24 Videoclips mit einer Spieldauer von 7 bis 46 Sekunden mit 33 vordefinierten Gefäßen evaluiert. Anhand dieses Tests wurde auch die Gefäßdetektionsrate eines Experten in der Third-Space Endoskopie ermittelt. Ergebnisse Der Algorithmus zeigte eine Gefäßdetektionsrate von 93,94% mit einer mittleren Rate an falsch positiven Signalen von 1,87 pro Minute. Die Gefäßdetektionsrate des Experten lag bei 90,1% ohne falsch positive Ergebnisse. In der internen Validierung an Einzelbildern wurde eine IoU von 63,47%, ein mittlerer Dice Score von 76,18% und eine Pixel Accuracy von 86,61% ermittelt. Zusammenfassung Dies ist der erste KI-Algorithmus, der für den Einsatz in der therapeutischen Endoskopie entwickelt wurde. Präliminäre Ergebnisse deuten auf eine mit Experten vergleichbare Detektion von Gefäßen während der Untersuchung hin. Weitere Untersuchungen sind nötig, um die Leistung des Algorithmus im Vergleich zum Experten genauer zu eruieren sowie einen möglichen klinischen Nutzen zu ermitteln. KW - Deep Learning KW - Third-Space Endoscopy Y1 - 2022 U6 - https://doi.org/10.1055/s-0042-1745652 VL - 60 IS - 04 PB - Thieme CY - Stuttgart ER - TY - JOUR A1 - Souza Jr., Luis Antonio de A1 - Pacheco, André G.C. A1 - Passos, Leandro A. A1 - Santana, Marcos Cleison S. A1 - Mendel, Robert A1 - Ebigbo, Alanna A1 - Probst, Andreas A1 - Messmann, Helmut A1 - Palm, Christoph A1 - Papa, João Paulo T1 - DeepCraftFuse: visual and deeply-learnable features work better together for esophageal cancer detection in patients with Barrett’s esophagus JF - Neural Computing and Applications N2 - Limitations in computer-assisted diagnosis include lack of labeled data and inability to model the relation between what experts see and what computers learn. Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their accountability and transparency level must be improved to transfer this success into clinical practice. The reliability of machine learning decisions must be explained and interpreted, especially for supporting the medical diagnosis. While deep learning techniques are broad so that unseen information might help learn patterns of interest, human insights to describe objects of interest help in decision-making. This paper proposes a novel approach, DeepCraftFuse, to address the challenge of combining information provided by deep networks with visual-based features to significantly enhance the correct identification of cancerous tissues in patients affected with Barrett’s esophagus (BE). We demonstrate that DeepCraftFuse outperforms state-of-the-art techniques on private and public datasets, reaching results of around 95% when distinguishing patients affected by BE that is either positive or negative to esophageal cancer. KW - Deep Learning KW - Speiseröhrenkrebs KW - Adenocarcinom KW - Endobrachyösophagus KW - Diagnose KW - Maschinelles Lernen KW - Machine learning KW - Adenocarcinoma KW - Object detector KW - Barrett’s esophagus KW - Deep Learning Y1 - 2024 U6 - https://doi.org/10.1007/s00521-024-09615-z VL - 36 SP - 10445 EP - 10459 PB - Springer CY - London ER - TY - JOUR A1 - Souza, Luis A. A1 - Pacheco, André G.C. A1 - de Souza, Alberto F. A1 - Oliveira-Santos, Thiago A1 - Badue, Claudine A1 - Palm, Christoph A1 - Papa, João Paulo T1 - TransConv: a lightweight architecture based on transformers and convolutional neural networks for adenocarcinoma and Barrett’s esophagus identification JF - Neural Computing and Applications N2 - Barrett’s esophagus, also known as BE, is commonly associated with repeated exposure to stomach acid. If not treated properly, it may evolve into esophageal adenocarcinoma, aka esophageal cancer. This paper proposes TransConv, a hybrid architecture that benefits from features learned by pre-trained vision transformers (ViTs) and convolutional neural networks (CNNs), followed by a shallow neural network composed of three normalizations, ReLU activations, and fully connected layers, and a SoftMax head to distinguish between BE and esophageal cancer. TransConv is designed to be training-lightweight, and for the ViT and CNN backbone models, weights are kept frozen during training, i.e., the primary goal of TransConv is to learn the weights of the fully connected layer from both backbones only, avoiding the burden of updating their weights but still learning their final descriptions for the lightweight convolutional model. We report promising results with low computational training costs in two datasets, one public and another private. From our achievements, TransConv was able to deliver balanced accuracy results around 85% and 86% for each evaluated dataset, respectively, in a design that required only 50 epochs of model training, a very reduced number compared to state-of-the-art conducted studies in the same domain. Y1 - 2025 U6 - https://doi.org/10.1007/s00521-025-11299-y IS - 37 SP - 15535 EP - 15546 PB - Springer ER - TY - INPR A1 - Rückert, Tobias A1 - Rückert, Daniel A1 - Palm, Christoph T1 - Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art N2 - In the field of computer- and robot-assisted minimally invasive surgery, enormous progress has been made in recent years based on the recognition of surgical instruments in endoscopic images. Especially the determination of the position and type of the instruments is of great interest here. Current work involves both spatial and temporal information with the idea, that the prediction of movement of surgical tools over time may improve the quality of final segmentations. The provision of publicly available datasets has recently encouraged the development of new methods, mainly based on deep learning. In this review, we identify datasets used for method development and evaluation, as well as quantify their frequency of use in the literature. We further present an overview of the current state of research regarding the segmentation and tracking of minimally invasive surgical instruments in endoscopic images. The paper focuses on methods that work purely visually without attached markers of any kind on the instruments, taking into account both single-frame segmentation approaches as well as those involving temporal information. A discussion of the reviewed literature is provided, highlighting existing shortcomings and emphasizing available potential for future developments. The publications considered were identified through the platforms Google Scholar, Web of Science, and PubMed. The search terms used were "instrument segmentation", "instrument tracking", "surgical tool segmentation", and "surgical tool tracking" and result in 408 articles published between 2015 and 2022 from which 109 were included using systematic selection criteria. Y1 - 2023 U6 - https://doi.org/10.48550/arXiv.2304.13014 ER - TY - JOUR A1 - Safi, Hila A1 - Bandic, Medina A1 - Niedermeier, Christoph A1 - Almudever, Carmen G. A1 - Feld, Sebastian A1 - Mauerer, Wolfgang T1 - Stacking the odds: full-stack quantum system design space exploration JF - EPJ Quantum Technology N2 - Design space exploration (DSE) plays an important role in optimising quantum circuit execution by systematically evaluating different configurations of compilation strategies and hardware settings. In this paper, we conduct a comprehensive investigation into the impact of various layout methods, qubit routing techniques, and optimisation levels, as well as device-specific properties such as different variants and strengths of noise and imperfections, the topological structure of qubits, connectivity densities, and back-end sizes. By spanning through these dimensions, we aim to understand the interplay between compilation choices and hardware characteristics. A key question driving our exploration is whether the optimal selection of device parameters, mapping techniques, comprising of initial layout strategies and routing heuristics can mitigate device induced errors beyond standard error mitigation approaches. Our results show that carefully selecting software strategies (e.g., mapping and routing algorithms) and tailoring hardware characteristics (such as minimising noise and leveraging topology and connectivity density) significantly improve the fidelity of circuit execution outcomes, and thus the expected correctness or success probability of the computational result. We provide estimates based on key metrics such as circuit depth, gate count and expected fidelity. Our results highlight the importance of hardware–software co-design, particularly as quantum systems scale to larger dimensions, and along the way towards fully error corrected quantum systems: Our study is based on computationally noisy simulations, but considers various implementations of quantum error correction (QEC) using the same approach as for other algorithms. The observed sensitivity of circuit fidelity to noise and connectivity suggests that co-design principles will be equally critical when integrating QEC in future systems. Our exploration provides practical guidelines for co-optimising physical mapping, qubit routing, and hardware configurations in realistic quantum computing scenarios. Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-85157 N1 - Corresponding author der OTH Regensburg: Hila Safi VL - 12 PB - Springer CY - Heidelberg ER - TY - CHAP A1 - Graf, Julian A1 - Hachani, Murad A1 - Fischer, Sebastian A1 - Hackenberg, Rudolf T1 - A heuristic packet processing model for improved encrypted network analysis T2 - CSCS '25: Proceedings of the 2nd Cyber Security in CarS Workshop N2 - Modern networked systems, such as those in the automotive sector, face increasing complexity and growing attack surfaces due to the rise of interconnected and data-driven technologies. Detecting malicious behavior in these environments requires efficient and scalable methods that can operate reliably despite limited resources and high communication volumes. This paper proposes a heuristic packet processing model designed to support intrusion detection based on structural and temporal characteristics of encrypted network traffic. The model follows a modular architecture consisting of four key phases: recording, sorting, prioritizing, and analyzing. At the core of the approach is the Polymetric Queueing Topology Space, a feature space that combines statistical and time series attributes derived from model structure and flow data. These features serve as input for machine learning models, which can effectively distinguish between benign and intrusion traffic patterns without relying on packet data beyond the transport layer. The approach was evaluated using the publicly available ToN_IoT dataset and demonstrated that reliable classification is achievable using a subset of the developed feature space that contains model-derived traffic features. We used Random Forest for supervised binary and multi-class classification achieving high accuracy scores of 99% for binary and 98% for multi-class classification. Additionally, for unsupervised anomaly detection, we created an Isolation Forest model accomplishing F1-scores of 0.92 for the benign and 0.96 for intrusion class. The architecture is designed to enable dynamic traffic prioritization and to offer a flexible foundation that can observe diverse network domains while maintaining efficient performance under constrained computational conditions. Y1 - 2025 U6 - https://doi.org/10.1145/3736130.3764510 PB - ACM CY - New York, USA ER - TY - GEN A1 - Hauser, Dominic A1 - Graf, Julian A1 - Fischer, Sebastian T1 - SEPP – Security Education and Penetration-Testing Platform for IoT T2 - Conference programme & abstract book N2 - The Internet of Things (IoT) is becoming a major part of our everyday lives, offering convenience and smarter solutions, but also bringing significant security challenges. While theoretical knowledge in IoT security is essential, studies have shown that practical content can be an essential part of internalizing understanding. To address this, we developed the Security Education and Penetration-Testing Platform (SEPP) as the practical component of an existing IoT security course at the OTH Regensburg. SEPP uses real IoT devices like smart locks, cameras, and plugs, simulating a smart home environment to make learning interactive and engaging. Students can explore vulnerabilities, conduct penetration tests, and document their findings through structured exercises. By working on tasks like network scanning, analyzing data traffic, and simulating attacks, students gain a deeper understanding of IoT security risks. Initial tests show that this approach helps students apply their theoretical knowledge and significantly improve their practical skills. This paper explains how SEPP was built, the exercises it offers, and why it’s an important step forward in teaching IoT security effectively. Furthermore, we aim to share the findings and tasks from this paper with other universities, providing them with a solid foundation to teach practical IoT security knowledge in their own courses. Y1 - 2025 UR - https://iafor.org/archives/conference-programmes/ece/ece-programme-2025.pdf SN - 2433-7544 SP - 101 PB - IAFOR ER - TY - GEN A1 - Fischer, Sebastian T1 - The persistent problems with cybersecurity : a negative example and an outlook on the Cyber Resilience Act Y1 - 2025 UR - https://www.iaria.org/conferences2025/filesDataSys25/Keynote_SebastianFischer_ThePersistentProblem.pdf PB - IARIA ER - TY - CHAP A1 - Bauer, Patrick A1 - Frikel, Jürgen T1 - BPConvNet: a deep learning based ρ-Filtered layergram reconstruction method for computed tomography T2 - AIP Conference Proceedings N2 - In this article, we address the reconstruction problem in computed tomography (CT) when dealing with sparse view data. Traditional approaches like filtered backprojection (FBP) often fail under these conditions, leading to streaking artifacts. We propose BPConvNet, a deep learning based version of the ρ-filtered layergram or backprojection filtration (BPF) technique (cf. [1]). Unlike FBP, the BPF method applies filtering (F) after backprojection (BP), hence the name. The proposed BPConvNet adapts the BPF workflow by substituting the filtering step with a residual convolutional neural network. Our numerical experiments demonstrate that BPConvNet is competitive to similar deep learning methods. Moreover, we explain that BPConvNet can be easily adapted to to different CT acquisition geometries, such as fan beam and 3D configurations KW - Convolutional neural network KW - Deep learning KW - Learning and learning models KW - Computed tomography Y1 - 2025 U6 - https://doi.org/10.1063/5.0286063 SN - 0094-243X VL - 3315 IS - 1 PB - AIP Publishing ER - TY - THES A1 - Schaffer, Josefa T1 - Precision in eye tracking: evaluation and improvement of accuracy N2 - This thesis will evaluate the accuracy of the Tobii Pro Glasses 3, which are wearable eye tracking glasses. A static and dynamic accuracy study will be conducted, utilising a poster and imitating realistic environments. The results will be evaluated using a self-developed sample-wise approach, trying to determine possible differences between static and dynamic conditions while wearing eye tracking glasses. In addition to that, a tool named glassesValidator, used to calculate data quality metrics of eye tracking glasses, will be evaluated on its accuracy and validity itself. In the static accuracy study, participants will focus, in reading order, on nine fixation targets on the poster for at least two seconds while using a chin rest to prevent head movement. In the dynamic accuracy study participants will be walking in a semicircular movement around the poster while focusing only on the central fixation target on the poster. The results for the static and dynamic study will be compared to each other, providing possible influence of different lighting conditions and the usage of contact lenses. The results provided by glassesValidator and the self-developed accuracy approach will also be compared to each other to evaluate and provide possible improvements to increase the accuracy of the tool. Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-85296 CY - Regensburg ER - TY - JOUR A1 - Neubauer, Katrin A1 - Fischer, Sebastian A1 - Hackenberg, Rudolf T1 - Security risk analysis of the cloud infrastructure of Smart Grid and IoT - 4-Level-Trust-Model as a security solution JF - International Journal on Advances in Internet Technology N2 - The digital transformation has found its way into business and private life. It consists of digitization and digitaliza- tion. Digitization means the technical process and digitalization is the socio-technological process. Technologies of digitization are Cloud Computing (CC), Internet of Things (IoT) and Smart Grid (SG), which are separate technologies. The increasing digitalization in the private sector and of the energy industry connect these technologies. Actually, there is no connection between the CC infrastructure and the SG infrastructure at the moment, because in Germany the SG is currently under construction. If one looks at the CC and IoT, it must be stated there is an connection between the IoT infrastructure and the CC infrastructure as a service provider. To connect the technologies CC, IoT and SG and also build an SG cloud for innovative services, the new laws for privacy must be implemented. For privacy and security analyses it is important to know which data can be stored and distributed on a cloud. To illustrate this analysis, we connect the SG infrastructure with the IoT. An IoT device (car charging station) should be able to transfer data to and from the SG. SG is a critical infrastructure and the IoT device a potential insecure device and network. We show the communication between the smart meter switching box and the IoT device and the data transferred between their clouds. The charging station is connected to the SG to get the current amount of renewable energy in the grid. This is necessary to create a new smart service. But this service also generates private data (e.g., name, address, payment details). The private data should not be transferred to the IoT cloud. For the connection of SG and IoT, availability, confidentiality and integrity must be ensured. A risk analysis over all the cloud connections, including the vulnerability and the ability of an attacker, the resulting risk and the 4-Level- Trust-Model for security assessment are developed. Furthermore, we show the application of the 4-Level-Trust-Model in this paper. Y1 - 2020 UR - https://www.researchgate.net/publication/346580224_Security_Risk_Analysis_of_the_Cloud_Infrastructure_of_Smart_Grid_and_IoT_-_4-Level-Trust-Model_as_a_Security_Solution UR - https://www.iariajournals.org/internet_technology/inttech_v13_n12_2020_paged.pdf VL - 13 IS - 1&2 SP - 11 EP - 20 ER - TY - CHAP A1 - Schwägerl, Felix A1 - Buchmann, Thomas A1 - Westfechtel, Bernhard T1 - Multi-variant model transformations - a problem statement T2 - Proceedings of the 11th International Conference on Evaluation of Novel Software Approaches to Software Engineering N2 - Model Transformations are a key element of Model-Driven Software Engineering. As soon as variability is involved, transformations become increasingly complicated. The lack of support for variability in model transformations impairs the acceptance of approaches to organized reuse such as software product lines. In this position paper, the general problem of multi-variant model transformations is formulated for MOF-based, XMI-serialized models. A simplistic case study is presented to specify the input and the expected output of such a transformation. Furthermore, requirements for tool support are defined, including a standardized representation of both multi-variant model instances and variability information, as well as an execution specification for multi-variant transformations. A literature review reveals that the problem is weakly identified and often solved using ad-hoc solutions; there exists no tool providing a general solution to the proposed problem statement. The observation s presented here may serve for the future development of standards and tools. KW - Model-Driven Software Engineering KW - Software Product Lines KW - Model Transformations KW - Code Generation Y1 - 2016 SN - 978-989-758-189-2 U6 - https://doi.org/10.5220/0005878702030209 SN - 2184-4895 SP - 203 EP - 209 PB - SCITEPRESS ER - TY - RPRT A1 - Alex, Karla A1 - Eichinger, Johanna A1 - Heyder, Clemens A1 - Kandlbinder, Agnes A1 - Loder, Sandra A1 - Rolfes, Vasilija A1 - Röttger, Sara A1 - Scharf, Anna A1 - Scorna, Ulrike A1 - Weigold, Stefanie ED - Cerullo, Laura ED - Gerhards, Helene ED - Weber, Karsten T1 - Abstractband I: In-vitro-Gametogenese (IVG) und artifizieller Uterus (AU) – Problemauslöser oder Problemlöser? Ethische, soziale und rechtliche Aspekte zukünftiger reproduktionsmedizinischer Verfahren BT - Klausurwoche für Early Career Researchers (19. September bis 23. September 2022, OTH Regensburg) Y1 - 2022 U6 - https://doi.org/10.13140/RG.2.2.30303.53928 N1 - Die Klausurwoche wird vom Bundesministerium für Bildung und Forschung (BMBF) (Förderkennzeichen: 01GP2185, Projektkürzel: IVG-AU-PP) finanziell gefördert. ER - TY - JOUR A1 - Hantsch, Carolin A1 - Westner, Markus A1 - Matschi, Markus T1 - IT/OT-Konvergenz: Status-Quo und Umsetzungsempfehlungen JF - HMD Praxis der Wirtschaftsinformatik N2 - Organisationen machen große Anstrengungen zur Verbesserung ihrer Ambidextrie, d. h. ihrer Fähigkeit sowohl Exploitationsziele (z. B. Effizienz, Vorhersagbarkeit) als auch Explorationsziele (z. B. Anpassbarkeit, Innovation) zu erreichen. Obwohl gemäß der Praxisliteratur Technologie in Software-Projekten dabei eine zentrale Rolle spielen kann, gibt es kaum Forschung dazu, wie technologische Faktoren Ambidexterie in Software-Projekten beeinflussen. In dieser Studie untersuchen wir die Rolle dreier technologischer Faktoren: kontinuierliche Integration, Standardisierung und Microservice-Architektur. Die Ergebnisse einer Mehrinformanten-Umfrage in 95 Projekten in der Schweiz und Dänemark zeigen, dass kontinuierliche Integration und Standardisierung mit höherer Ambidexterie einhergehen, während der Zusammenhang zwischen Microservice-Architektur und Ambidextrie nicht signifikant ist. Unser Artikel geht über die existierende Forschung hinaus, indem er zeigt, dass kontinuierliche Integration und Standardisierung nicht nur Exploitation, sondern auch Exploration fördern können. Wir diskutieren auch Gründe für den insignifikanten Zusammenhang zwischen Microservice-Architektur und Ambidextrie. N2 - This study examines IT/OT Convergence in 31 predominantly large German companies. It presents an “IT/OT Governance Framework” as a specific model for successful convergence implementation. The analysis reveals a heterogeneous level of convergence maturity across the examined companies, the majority of which are at the beginning or in the implementation phase. Close cooperation between IT and OT is favored, but without IT completely taking over OT. An IT/OT tandem consisting of a CIO and a central OT responsible person is seen as the ideal way to take responsibility. Six specific recommendations were derived from the interviews and included in the IT/OT Governance Framework developed. These include ensuring top management support, using security as a driver, introducing a central OT responsible person, bringing IT and OT teams physically closer together, continuously demonstrating the added value of convergence, and taking a step-by-step approach to implementation. KW - IT/OT Convergence KW - Operational Technology KW - OT KW - Governance Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-77937 N1 - Corresponding author der OTH Regensburg: Markus Westner PB - Springer Nature ER - TY - JOUR A1 - Schultz, Maximilian A1 - Hauer, Franziska A1 - Haug, Sonja A1 - Weber, Karsten T1 - Künstliche Intelligenz in sozialen Organisationen implementieren BT - von der Einzelinitiative zur Strategie JF - Sozialwirtschaft N2 - Die Digitalisierung wird in der Gesellschaft seit vielen Jahren thematisiert. Durch Künstliche Intelligenz (KI) hat diese Diskussion einen zusätzlichen Anstoß bekommen. Inbesondere für gemeinwohlorientierte Organisationen bieten sich Potenziale sowie Risiken. Sie könnten zur Entlastung von Fachkräften, zur Verbesserung organisatorischer Abläufe und zur Entwicklung neuer Unterstützungsangebote beitragen. Angesichts chronischer Herausforderungen wie Fachkräftemangel, Ressourcenknappheit und wachsender Komplexität von Arbeitsprozessen erscheint KI daher als möglicher Hebel für eine tiefgreifende Transformation. KW - KI KW - Sozialwirtschaft Y1 - 2025 U6 - https://doi.org/10.5771/1613-0707-2025-5-35 VL - 35 IS - 5 SP - 35 EP - 38 PB - nomos Verlag ER - TY - GEN A1 - Weber, Karsten T1 - KI-Nutzung für (psychisch) kranke Menschen: Auswirkungen auf das Professionsverständnis der Fachkräfte Y1 - 2024 ER - TY - GEN A1 - Weber, Karsten T1 - Elektrisch + digital = nachhaltig? Welche Nachhaltigkeitsgewinne haben neue Mobilitätformen? Y1 - 2023 ER - TY - GEN A1 - Weber, Karsten T1 - Medizin, Mobilität, Medien: Digitaler Wandel als Chance und Herausforderung Y1 - 2023 ER - TY - GEN A1 - Weber, Karsten T1 - Der Mars zwischen Science und Fiction Y1 - 2023 ER - TY - GEN A1 - Weber, Karsten T1 - Nachhaltigkeit ist (k)eine Frage der Philosophie Y1 - 2025 ER - TY - GEN A1 - Weber, Karsten T1 - Mögliche Zukünfte der Künstlichen Intelligenz Y1 - 2023 ER - TY - GEN A1 - Weber, Karsten T1 - Mögliche Zukünfte der Künstlichen Intelligenz Y1 - 2023 ER - TY - GEN A1 - Weber, Karsten T1 - Der Mensch nach Maß?! Y1 - 2023 ER - TY - GEN A1 - Weber, Karsten T1 - The weapons of war and conflict are technology BT - Security from the viewpoint of Technology Assessment Y1 - 2023 ER - TY - GEN A1 - Weber, Karsten T1 - Social science research, research on acceptance, and applied ethics on technology and artificial intelligence in the health sector Y1 - 2024 ER - TY - GEN A1 - Weber, Karsten T1 - KI und Roboter in der Science Fiction – eine (augenzwinkernde) Einführung zur KI-Forschung Y1 - 2024 ER - TY - GEN A1 - Weber, Karsten T1 - Die üblichen Verdächtigen – automatisierte Strafverfolgung aus Perspektive der Technikfolgenabschätzung und Technikbewertung Y1 - 2024 ER - TY - GEN A1 - Weber, Karsten T1 - Nutzungsdauer und Recycling softwareabhängiger Geräte – Herausforderungen und mögliche Lösungsansätze Y1 - 2023 ER - TY - GEN A1 - Weber, Karsten T1 - Digitalisierung, KI, Robotik – Visionen für die Gesundheitsversorgung von morgen Y1 - 2024 ER - TY - GEN A1 - Weber, Karsten T1 - Verstand, Vernunft, Seele: Was unterscheidet KI-Systeme von Menschen? Y1 - 2024 ER - TY - GEN A1 - Weber, Karsten T1 - KI und Big Data: Wenn ein Hype zum ethischen Stolperstein wird Y1 - 2025 ER - TY - GEN A1 - Weber, Karsten T1 - Aspekte der sozialen Nachhaltigkeit am Beispiel des Einsatzes von KI. Ergebnisse einer Befragung von zivilgesellschaftlichen Organisationen in der Grenzregion im Bereich Umweltschutz, Sozial- und Gesundheitswesen T2 - Aspekty sociální udržitelnosti na příkladu využití umělé inteligence. Výsledky průzkumu mezi občanskými organizacemi v pohraniční oblasti v oblasti ochrany životního prostředí, sociálních věcí a zdravotnictví a lidských práv Y1 - 2025 ER - TY - GEN A1 - Weber, Karsten T1 - Künstliche Intelligenz für Nichtregierungsorganisationen (KINiro) – Bedarf, Akzeptanz und Umsetzungsmöglichkeiten Y1 - 2025 ER - TY - GEN A1 - Weber, Karsten T1 - Zwischen Fortschritt und Verantwortung: KI, Nachhaltigkeit und wir Y1 - 2025 ER - TY - GEN A1 - Weber, Karsten T1 - Künstliche Intelligenz: Ökonomische und ethische Aspekte Y1 - 2025 ER - TY - GEN A1 - Weber, Karsten T1 - KI ./. Demokratie BT - Ein kompliziertes Verhältnis Y1 - 2024 ER - TY - CHAP A1 - Vetter, Miriam A1 - Haug, Sonja A1 - Weber, Karsten ED - BBSR – Bundesinstitut für Bau-, Stadt- und Raumforschung im Bundesamt für Bauwesen und Raumordnung, T1 - Die Zukunft der Notfallerkennung in Haushalten älterer Menschen : eine Technikbewertung T2 - Demografische Alterungsprozesse : Chancen und Herausforderungen für die Regionalentwicklung N2 - Die meisten älteren Personen wünschen sich ein Altern in der Häuslichkeit, auch bei zunehmendem Anteil an Pflegebedürftigen in der Bevölkerung. Der Pflegenotstand im ländlichen Raum und im ambulanten Bereich stellt die häusliche Versorgung jedoch vor Herausforderungen. Die Digitalisierung soll dabei Abhilfe schaffen. Hausnotrufsysteme sind eine weit verbreitete Form digitaler Assistenzsysteme, die auf Knopfdruck Hilferufe an Hausnotrufzentralen übermitteln. Sie stehen jedoch in der Kritik, da die Handsender aus Angst vor Stigmatisierung von den Pflegebedürftigen oft abgelegt und daher trotz Notlage nicht genutzt werden. Neben vielfältiger Forschung zu digitalen Assistenzsystemen werden zunehmend technische Prototypen zu Smart-Meter-Anwendungen zur Notfallerkennung für ältere Menschen entwickelt, die mithilfe von Stromverbrauchsdaten und Künstlicher Intelligenz auf Inaktivität und somit mögliche Notfälle schließen. Smart-Meter sind intelligente Messsysteme, die für bestimmte Privathaushalte gesetzlich verpflichtend eingeführt werden, um Netzstabilität zu gewährleisten. Dieser Beitrag stellt erste Ergebnisse einer Technikbewertung mithilfe eines Mixed-Method-Ansatzes vor. Ein Drittel der Expertinnen und Experten sieht eine positive Nutzungsintention. Die Bevölkerungsbefragung zeigt Unentschlossenheit bezüglich der neuen Technologie. Eine Nutzungsintention besteht vor allem bei jüngeren technikaffinen Personen, d.h. den älteren Menschen der Zukunft. KW - Notfallerkennung KW - Smart Meter KW - Akzeptanz Y1 - 2025 U6 - https://doi.org/10.58007/gcpc-7j48 SN - 1868-0097 SP - 126 EP - 136 PB - BBSR – Bundesinstitut für Bau-, Stadt- und Raumforschung im Bundesamt für Bauwesen und Raumordnung (BBR) CY - Bonn ER - TY - JOUR A1 - Bedbur, Stefan A1 - Kamps, Udo A1 - Schmiedt, Anja Bettina T1 - A flexible model of ordered random variables for non-metallic inclusions in steels and related statistical inference JF - Applied Mathematical Modelling N2 - In a data set of non-metallic inclusion sizes in samples from engineering steel, common order statistics fail to serve as a suitable model for ascendingly ordered measurements within single samples. Therefore, a flexible model of ordered random variables is proposed, which allows for changes of distributions described by model parameters. Joint maximum likelihood estimation of these parameters and the shape parameter of an underlying left-truncated Weibull distribution is considered, and a model test is developed for the null-hypothesis of common order statistics being an adequate model. To overcome small data situations, a link-function approach is examined in order to reduce the number of involved model parameters as well as to propose to use a link-function parameter as a material indicator. An asymptotic test is provided to check for the presence of a linear link function, and tests for hypotheses about two link-function parameters are studied. Moreover, the construction of simultaneous confidence regions for the link-function parameters as well as of confidence bands for the entire graph of the link function are presented. Throughout, the findings are applied to the real metallurgical data set. Similar problems and data structures arise in other fields of material science and applications such as geology. Y1 - 2026 U6 - https://doi.org/10.1016/j.apm.2025.116284 SN - 0307-904X N1 - Corresponding author der OTH Regensburg: Anja Bettina Schmiedt VL - 149 PB - Elsevier ER - TY - JOUR A1 - Schmiedt, Anja B. A1 - Empacher, Christina A1 - Kamps, Udo T1 - One- and two-sided prediction intervals for future Pareto record values with applications JF - Journal of Statistical Theory and Applications N2 - Based on upper record values in a series of observations over time from a Pareto distribution, an exact and several approximate one-sided and two-sided prediction intervals for the next record value (or for another future record value) to appear are studied and compared by means of an extensive simulation study. The performances of the prediction intervals are evaluated and recommendations are proposed for what method should be used in a given situation with respect to the form of the prediction interval on the one hand and to the tail behaviour on the other. The proposed methods are applied to insurance, environmental and sports data, where the respectively fitted Pareto distributions show a different tail behaviour. As a result, it is seen that although the number of observed record values in the presented real data applications is rather small, as it is usually the case, the selected prediction intervals are of practical use. KW - Interval prediction KW - Pareto distribution KW - Real data analyses KW - Record values KW - Simulation study Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-81605 SN - 2214-1766 N1 - Corresponding author der OTH Regensburg: Anja B. Schmiedt PB - Springer ER - TY - JOUR A1 - Dotter, Caroline A1 - Haug, Sonja A1 - Schnell, Rainer A1 - Raptis, Georgios A1 - Weber, Karsten T1 - Sharing health data for research purposes: results of a population survey in Germany JF - BMC health services research N2 - BACKGROUND: Increased use of health data has the potential to improve both health care and health policies. Several recent policy initiatives at the European and German legislative levels aim to increase the primary and secondary use of health data. However, little is known about general population views on health data access for research. Most studies are based on subsets defined by specific illnesses. METHODS: We commissioned a national computer-assisted dual-frame telephone survey (landline and mobile). Logit estimation models were used to identify predictors of willingness to provide access to health data to different organizations (universities in Germany, universities worldwide, German government organizations, pharmaceutical companies). RESULTS: A high willingness to share health data for research purposes is observed, depending on the specific data recipient. The willingness is highest for research at universities in Germany and German governmental organizations, and lowest regarding research by pharmaceutical companies. The main drivers for sharing health data are the level of trust in public institutions, the respondents' assessment of the seriousness and likelihood of data misuse, and the level of digital literacy. Age, gender, and level of education have small effects and do not determine the willingness to share health data for all organizations. CONCLUSION: We present evidence from a random sample of the German population. The results indicate widespread support among the population for providing access to health data for research purposes. Similar to findings in other countries, the willingness depends strongly on the recipient of the data. This paper evaluates the impact of various determinants - identified in previous qualitative and quantitative research - on the willingness of the German population to share health data. While previous studies have found that patients are generally more willing to share health data, we found that the presence of a medical precondition does not translate into respondents' unequivocal support for health data sharing. We identify privacy concerns, general trust, and digital literacy as key factors influencing the willingness to share health data. Therefore, policymakers and stakeholders need to ensure and communicate the necessary privacy protection measures to increase the willingness of the German population to share health data. Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-81622 N1 - Corresponding author der OTH Regensburg: Caroline Dotter VL - 25 PB - BMC ER - TY - CHAP A1 - Schwägerl, Felix A1 - Uhrig, Sabrina T1 - Tool Support for the Evaluation of Matching Algorithms in the Eclipse Modeling Framework T2 - Proceedings of the 1st International Conference on Model-Driven Engineering and Software Development, February 19-21, 2013, in Barcelona, Spain, Vol. 1 N2 - In the field of model-driven development, sophisticated support for comparing model versions is urgently needed. Unfortunately, algorithms for model matching have been rarely evaluated so far. This paper deals with two extensions to the Eclipse Modeling Framework (EMF) that facilitate the evaluation of matching algorithms for EMF models, with the goal to combine user involvement and automated testing in the evaluation process. First a tree editor is presented that allows for the manual and semi-automated creation of match models which formalize the intended matching result. Second a benchmarking procedure is implemented which – given the intended match and the actual results of matching algorithms – automatically derives the number of a and b errors in a target-performance comparison. These results are valuable for drawing conclusions about the specific qualities of matching algorithms or for finding an adequate set of parameters for a configurable algorithm. KW - EMF Models KW - Model Comparison KW - Matching Algorithms KW - Evaluation Y1 - 2013 SN - 978-989-8565-42-6 U6 - https://doi.org/10.5220/0004310801010110 SN - 2184-4348 SP - 101 EP - 110 PB - SciTePress - Science and and Technology Publications ER - TY - CHAP A1 - Buchmann, Thomas A1 - Schwägerl, Felix T1 - Using Meta-code Generation to Realize Higher-order Model Transformations T2 - Proceedings of the 8th International Joint Conference on Software Technologies, July 29-31, 2013, in Reykjavík, Iceland, Vol. 1 N2 - Model-driven engineering is a wide-spread paradigm in modern software engineering. During the last couple of years, many tools and languages have been developed, which are especially designed for model transformations — a discipline which is needed in many model-driven engineering approaches. While most of the existing model-to-model tools and languages are tailored towards batch transformations for specific model instances, they lack support for generic transformation problems, where the metamodel is unknown beforehand. In this paper we present a two-step meta-code generation approach that derives a metamodel-specific modelto- model transformation from a model-to-text transformation. The approach has been successfully applied to the problem of product derivation in model-driven software product lines. KW - Model-driven Development KW - M2M Transformations KW - Model Transformations KW - Higher-order Transformations KW - Software Product Lines Y1 - 2013 SN - 978-989-8565-68-6 U6 - https://doi.org/https://doi.org/10.5220/0004522305360541 SP - 536 EP - 541 PB - SciTePress - Science and and Technology Publications ER - TY - JOUR A1 - Winter, Lina A1 - Artinger, Annalena A1 - Böck, Hendrik A1 - Ramakrishnan, Vignesh A1 - Reible, Bruno A1 - Albin, Jan A1 - Schüffler, Peter J. A1 - Raptis, Georgios A1 - Brochhausen, Christoph T1 - Developing a smart and scalable tool for histopathological education—PATe 2.0 JF - Journal of Pathology Informatics N2 - Digital microscopy plays a crucial role in pathology education, providing scalable and standardized access to learning resources. In response, we present PATe 2.0, a scalable redeveloped web-application of the former PATe system from 2015. PATe 2.0 was developed using an agile, iterative process and built on a microservices architecture to ensure modularity, scalability, and reliability. It integrates a modern web-based user interface optimized for desktop and tablet use and automates key workflows such as whole-slide image uploads and processing. Performance tests demonstrated that PATe 2.0 significantly reduces tile request times compared to PATe, despite handling larger tiles. The platform supports open formats like DICOM and OpenSlide, enhancing its interoperability and adaptability across institutions. PATe 2.0 represents a robust digital microscopy solution in pathology education enhancing usability, performance, and flexibility. Its design enables future integration of research algorithms and highlights it as a pivotal tool for advancing pathology education and research. Y1 - 2026 U6 - https://doi.org/10.1016/j.jpi.2025.100535 SN - 2153-3539 VL - 20 PB - Elsevier ER - TY - GEN A1 - Weber, Karsten T1 - Künstliche Intelligenz als vielschichtige Herausforderung Y1 - 2025 ER - TY - RPRT A1 - Bhardwaj, Prakhar A1 - Hauer, Franziska A1 - Mücke, Vanessa A1 - Dotter, Caroline A1 - Haug, Sonja A1 - Weikl, Simone A1 - Weber, Karsten T1 - Recording and understanding multi- and intermodal mobility - a review N2 - The transition to sustainable mobility requires detailed knowledge of actual mobility behavior. Recording mobility behavior through conventional, aggregated, or retrospective survey methods (e.g., counting stations, travel diaries) may yield incomplete insights. In addition, these methods are often expensive, prone to error, or not scalable. The aim of this literature review is to provide a systematic overview of the current state of the literature on recording and analysis of individual mobility data and, based on this, to identify possible research gaps and potential. The exploratory literature review includes social science surveys on multi- and intermodal mobility behavior, as well as an overview of possible ways to analyze travel data. The focus of the latter is on identifying relevant data sources and methodological approaches for segmentation, identification of transport mode, and trip purpose determination. Results from recent studies show that sensor-based data collection alone is not sufficient to fully and accurately map mobility behavior. One possible solution is a “best-of-both-worlds” approach that combines passive, automatic mobility data collection via smartphone sensors with targeted user corrections and additions (“human-in-the-loop”) within an app. The literature review shows that this approach yields the best data quality. Combining mobility surveys with passive collection of mobility data provides a comprehensive, accurate, and complete overview of mobility behavior. KW - multimodal mobility KW - mobility Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-87427 CY - Regensburg ER - TY - GEN A1 - Hauer, Franziska A1 - Haug, Sonja A1 - Scharf, Anna A1 - Schultz, Maximilian A1 - Weber, Karsten T1 - Quantitative Befragung zu Künstlicher Intelligenz für Nichtregierungsorganisationen -Fragebogen und Codebuch N2 - Künstliche Intelligenz (KI) stellt ein wichtiges Thema der Digitalisierung dar und hat das Potenzial viele Arbeitsfelder grundlegend zu verändern. Mit dem Einsatz von KI wird die Hoffnung verbunden durch Automatisierung Zeit und Geld zu sparen. Dies könnte besonders interessant für Organisationen sein, die wenig Ressourcen zur Verfügung haben, da sie nicht gewinnorientiert oder ehrenamtlich arbeiten. Dazu zählen Nichtregierungsorganisationen (NRO), die einen wichtigen Bestandteil der Zivilgesellschaft darstellen. Aufgrund der komplexer werdenden Arbeit von NROs scheint KI Möglichkeiten zur Bewältigung aktueller und zukünftiger Herausforderungen zu bieten. Über den Einsatz von KI in NROs ist derzeit jedoch wenig bekannt. Es wurde eine quantitative Querschnittstudie deutscher Nichtregierungsorganisationen (NRO) zum Thema KI durchgeführt. Der Web-Survey baut auf den Erkenntnissen des Scoping Reviews und der qualitativen Interviews auf und vertieft den Einblick in die Bereiche aktuelle Nutzung von KI, Wissenstand in den Organisationen sowie vorhandene und noch benötigte Ressourcen. Dafür wurden 343 NROs verschiedener Größen und Handlungsfelder befragt und die Ergebnisse quantitativ analysiert. KW - Künstliche Intelligenz KW - NGO Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-87066 N1 - Hauer, Franziska; Haug, Sonja; Scharf, Anna; Schultz, Maximilian; Weber, Karsten (2024): Künstliche Intelligenz für Nichtregierungsorganisationen - Quantitative Erforschung der Umsetzung in deutschen NROs. 3. Arbeitspapier. OTH Regensburg. Regensburg. DOI: 10.13140/RG.2.2.11094.66883 CY - Regensburg ER - TY - RPRT A1 - Bhardwaj, Prakhar A1 - Hauer, Franziska A1 - Mücke, Vanessa A1 - Dotter, Caroline A1 - Haug, Sonja A1 - Weikl, Simone A1 - Weber, Karsten T1 - Multi- und intermodale Mobilität erfassen und verstehen - eine Literaturübersicht N2 - Die Transformation zu nachhaltiger Mobilität erfordert eine detaillierte Erfassung des tatsächlichen Mobilitätsverhaltens, was durch herkömmliche, aggregierte oder retrospektive Erhebungsmethoden (z.B. Zählstellen, Wegetagebücher) nicht vollständig gelingt. Zudem sind diese Methoden oft teuer, fehleranfällig oder nicht nachhaltig skalierbar. Ziel dieser Literaturübersicht ist es, einen systematischen Überblick über den aktuellen Forschungsstand zur Erhebung und Analyse individueller Mobilitätsdaten zu geben und darauf aufbauend mögliche Forschungslücken sowie Potentiale zu identifizieren. Die explorative Literaturrecherche umfasst sozialwissenschaftliche Erhebungen zu multi- und intermodalem Mobilitätsverhalten, sowie einen Überblick über Analysemöglichkeiten von Wegedaten. Der Fokus liegt auf der Identifikation relevanter Datenquellen und methodischer Ansätze zur Segmentierung, Verkehrsmittelerkennung und Wegezweckbestimmung. Ergebnisse aus aktuellen Studien verdeutlichen, dass ausschließlich sensorgestützte Datenerhebungen bisher nicht ausreichen, um Mobilitätsverhalten vollumfänglich und präzise abzubilden. Eine mögliche Lösung ist ein "Best-of-both-worlds-Ansatz, der die passive, automatische Mobilitätsdatenerfassung via Smartphone-Sensorik mit gezielten Nutzerkorrekturen und Ergänzungen ("Human-in-the-Loop") innerhalb einer App kombiniert. Die Literaturauswertung zeigt, dass auf diesem Weg die beste Datenqualität gewonnen werden kann. Durch die Kombination von Mobilitätsbefragung mit passiver Erhebung von Mobilitätsdaten kann ein umfassender, möglichst vollständiger und korrekter Überblick über das Mobilitätsverhalten gewonnen werden. KW - KI KW - Mobilität Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-87415 CY - Regensburg ER - TY - CHAP A1 - Franz, Maja A1 - Winker, Tobias A1 - Groppe, Sven A1 - Mauerer, Wolfgang T1 - Hype or Heuristic? Quantum Reinforcement Learning for Join Order Optimisation T2 - 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), 15-20 September 2024, Montreal, QC, Canada N2 - Identifying optimal join orders (JOs) stands out as a key challenge in database research and engineering. Owing to the large search space, established classical methods rely on approximations and heuristics. Recent efforts have successfully explored reinforcement learning (RL) for JO. Likewise, quantum versions of RL have received considerable scientific attention. Yet, it is an open question if they can achieve sustainable, overall practical advantages with improved quantum processors. In this paper, we present a novel approach that uses quantum reinforcement learning (QRL) for JO based on a hybrid variational quantum ansatz. It is able to handle general bushy join trees instead of resorting to simpler left-deep variants as compared to approaches based on quantum(-inspired) optimisation, yet requires multiple orders of magnitudes fewer qubits, which is a scarce resource even for post-NISQ systems. Despite moderate circuit depth, the ansatz exceeds current NISQ capabilities, which requires an evaluation by numerical simulations. While QRL may not significantly outperform classical approaches in solving the JO problem with respect to result quality (albeit we see parity), we find a drastic reduction in required trainable parameters. This benefits practically relevant aspects ranging from shorter training times compared to classical RL, less involved classical optimisation passes, or better use of available training data, and fits data-stream and low-latency processing scenarios. Our comprehensive evaluation and careful discussion delivers a balanced perspective on possible practical quantum advantage, provides insights for future systemic approaches, and allows for quantitatively assessing trade-offs of quantum approaches for one of the most crucial problems of database management systems. Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-76877 N1 - Corresponding author der OTH Regensburg: Maja Franz SP - 409 EP - 420 PB - IEEE ER - TY - JOUR A1 - Krüger, Tom A1 - Mauerer, Wolfgang T1 - Out of the Loop: Structural Approximation of Optimisation Landscapes and non-Iterative Quantum Optimisation JF - Quantum N2 - The Quantum Approximate Optimisation Algorithm (QAOA) is a widely studied quantum-classical iterative heuristic for combinatorial optimisation. While QAOA targets problems in complexity class NP, the classical optimisation procedure required in every iteration is itself known to be NP-hard. Still, advantage over classical approaches is suspected for certain scenarios, but nature and origin of its computational power are not yet satisfactorily understood. By introducing means of efficiently and accurately approximating the QAOA optimisation landscape from solution space structures, we derive a new algorithmic variant of unit-depth QAOA for two-level Hamiltonians (including all problems in NP): Instead of performing an iterative quantum-classical computation for each input instance, our non-iterative method is based on a quantum circuit that is instance-independent, but problem-specific. It matches or outperforms unit-depth QAOA for key combinatorial problems, despite reduced computational effort. Our approach is based on proving a long-standing conjecture regarding instance-independent structures in QAOA. By ensuring generality, we link existing empirical observations on QAOA parameter clustering to established approaches in theoretical computer science, and provide a sound foundation for understanding the link between structural properties of solution spaces and quantum optimisation. Y1 - 2025 U6 - https://doi.org/10.22331/q-2025-11-06-1903 N1 - Die Preprint-Version ist ebenfalls in diesem Repositorium verzeichnet unter: https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/start/2/rows/10/sortfield/score/sortorder/desc/searchtype/simple/query/Out+of+the+Loop/docId/7938 VL - 9 PB - Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften ER - TY - CHAP A1 - Schönberger, Manuel A1 - Trummer, Immanuel A1 - Mauerer, Wolfgang T1 - Large-Scale Multiple Query Optimisation with Incremental Quantum(-Inspired) Annealing T2 - Proceedings of the ACM on Management of Data N2 - Multiple-query optimization (MQO) seeks to reduce redundant work across query batches. While MQO offers opportunities for dramatic performance improvements, the problem is NP-hard, limiting the sizes of problems that can be solved on generic hardware. We propose to leverage specialized hardware solvers for optimization, such as Fujitsu's Digital Annealer (DA), to scale up MQO to problem sizes formerly out of reach. We present a novel incremental processing approach that combines classical computation with DA acceleration. By efficiently partitioning MQO problems into sets of partial problems, and by applying a dynamic search steering strategy that reapplies initially discarded information to incrementally process individual problems, our method overcomes capacity limitations, and scales to extremely large MQO instances (up to νm1000 queries). A thorough and comprehensive empirical evaluation finds our method substantially outperforms existing approaches. Our generalisable framework lays the ground for other database use-cases on quantum-inspired hardware, and bridges towards future quantum accelerators. Y1 - 2025 U6 - https://doi.org/10.1145/3749171 VL - 3 IS - 4 PB - ACM ER - TY - INPR A1 - Köster, Dominik A1 - Porkert, Florian A1 - Volbert, Klaus T1 - Multi-Day Scheduling for Electric Vehicle Routing: A Novel Model and Comparison Of Metaheuristics N2 - The increasing use of electric vehicles (EVs) requires efficient route planning solutions that take into account the limited range of EVs and the associated charging times, as well as the different types of charging stations. In this work, we model and solve an electric vehicle routing problem (EVRP) designed for a cross-platform navigation system for individual transport. The aim is to provide users with an efficient route for their daily appointments and to reduce possible inconveniences caused by charging their EV. Based on these assumptions, we propose a multi-day model in the form of a mixed integer programming (MIP) problem that takes into account the vehicle's battery capacity and the time windows of user's appointments. The model is solved using various established metaheuristics, including tabu search (TS), adaptive large neighborhood search (ALNS), and ant colony optimization (ACO). Furthermore, the performance of the individual approaches is analyzed using generated ensembles to estimate their behavior in reality and is compared with the exact results of the Google OR-Tools solver. 6 pages, 5 figures KW - Charging stations KW - Electric vehicles KW - Metaheuristics KW - Smart cities KW - Vehicle routing Y1 - 2025 U6 - https://doi.org/10.48550/arXiv.2512.21346 ER -