TY - CHAP A1 - Zehner, Alexander A1 - Szalo, Alexander Eduard A1 - Palm, Christoph T1 - GraphMIC: Easy Prototyping of Medical Image Computing Applications T2 - Interactive Medical Image Computing (IMIC), Workshop at the Medical Image Computing and Computer Assisted Interventions (MICCAI 2015), 2015, Munich N2 - GraphMIC is a cross-platform image processing application utilizing the libraries ITK and OpenCV. The abstract structure of image processing pipelines is visually represented by user interface components based on modern QtQuick technology and allows users to focus on arrangement and parameterization of operations rather than implementing the equivalent functionality natively in C++. The application's central goal is to improve and simplify the typical workflow by providing various high level features and functions like multi threading, image sequence processing and advanced error handling. A built-in python interpreter allows the creation of custom nodes, where user defined algorithms can be integrated to extend basic functionality. An embedded 2d/3d visual-izer gives feedback of the resulting image of an operation or the whole pipeline. User inputs like seed points, contours or regions are forwarded to the processing pipeline as parameters to offer semi-automatic image computing. We report the main concept of the application and introduce several features and their implementation. Finally, the current state of development as well as future perspectives of GraphMIC are discussed KW - Bildverarbeitung KW - Medizin Y1 - 2015 U6 - https://doi.org/10.13140/RG.2.1.3718.4725 N1 - Open-Access-Publikation SP - 395 EP - 400 ER - TY - CHAP A1 - Weber, Joachim A1 - Doenitz, Christian A1 - Brawanski, Alexander A1 - Palm, Christoph T1 - Data-Parallel MRI Brain Segmentation in Clinicial Use BT - Porting FSL-Fastv4 to GPGPUs T2 - Bildverarbeitung für die Medizin 2015; Algorithmen - Systeme - Anwendungen; Proceedings des Workshops vom 15. bis 17. März 2015 in Lübeck N2 - Structural MRI brain analysis and segmentation is a crucial part in the daily routine in neurosurgery for intervention planning. Exemplarily, the free software FSL-FAST (FMRIB’s Segmentation Library – FMRIB’s Automated Segmentation Tool) in version 4 is used for segmentation of brain tissue types. To speed up the segmentation procedure by parallel execution, we transferred FSL-FAST to a General Purpose Graphics Processing Unit (GPGPU) using Open Computing Language (OpenCL) [1]. The necessary steps for parallelization resulted in substantially different and less useful results. Therefore, the underlying methods were revised and adapted yielding computational overhead. Nevertheless, we achieved a speed-up factor of 3.59 from CPU to GPGPU execution, as well providing similar useful or even better results. KW - Brain Segmentation KW - Magnetic Resonance Imaging KW - Parallel Execution KW - Voxel Spacing KW - General Purpose Graphic Processing Unit KW - Kernspintomografie KW - Gehirn KW - Bildsegmentierung KW - Parallelverarbeitung Y1 - 2015 U6 - https://doi.org/10.1007/978-3-662-46224-9_67 SP - 389 EP - 394 PB - Springer CY - Berlin ER - TY - GEN A1 - Maier, Johannes A1 - Weiherer, Maximilian A1 - Huber, Michaela A1 - Palm, Christoph ED - Handels, Heinz ED - Deserno, Thomas M. ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Palm, Christoph ED - Tolxdorff, Thomas T1 - Abstract: Imitating Human Soft Tissue with Dual-Material 3D Printing T2 - Bildverarbeitung für die Medizin 2019, Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 17. bis 19. März 2019 in Lübeck N2 - 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. KW - Handchirurgie KW - 3D-Druck KW - Lernprogramm KW - HaptiVisT Y1 - 2019 SN - 978-3-658-25325-7 U6 - https://doi.org/10.1007/978-3-658-25326-4_48 SP - 218 PB - Springer Vieweg CY - Wiesbaden ER - TY - GEN A1 - Rückert, Tobias A1 - Rieder, Maximilian A1 - Rauber, David A1 - Xiao, Michel A1 - Humolli, Eg A1 - Feussner, Hubertus A1 - Wilhelm, Dirk A1 - Palm, Christoph T1 - Augmenting instrument segmentation in video sequences of minimally invasive surgery by synthetic smoky frames T2 - International Journal of Computer Assisted Radiology and Surgery KW - Surgical instrument segmentation KW - smoke simulation KW - unpaired image-to-image translation KW - robot-assisted surgery Y1 - 2023 U6 - https://doi.org/10.1007/s11548-023-02878-2 VL - 18 IS - Suppl 1 SP - S54 EP - S56 PB - Springer Nature 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 - 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 - 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 -