@article{HuttererHattingenPalmetal., author = {Hutterer, Markus and Hattingen, Elke and Palm, Christoph and Proescholdt, Martin Andreas and Hau, Peter}, title = {Current standards and new concepts in MRI and PET response assessment of antiangiogenic therapies in high-grade glioma patients}, series = {Neuro-Oncology}, volume = {17}, journal = {Neuro-Oncology}, number = {6}, doi = {10.1093/neuonc/nou322}, pages = {784 -- 800}, abstract = {Despite multimodal treatment, the prognosis of high-grade gliomas is grim. As tumor growth is critically dependent on new blood vessel formation, antiangiogenic treatment approaches offer an innovative treatment strategy. Bevacizumab, a humanized monoclonal antibody, has been in the spotlight of antiangiogenic approaches for several years. Currently, MRI including contrast-enhanced T1-weighted and T2/fluid-attenuated inversion recovery (FLAIR) images is routinely used to evaluate antiangiogenic treatment response (Response Assessment in Neuro-Oncology criteria). However, by restoring the blood-brain barrier, bevacizumab may reduce T1 contrast enhancement and T2/FLAIR hyperintensity, thereby obscuring the imaging-based detection of progression. The aim of this review is to highlight the recent role of imaging biomarkers from MR and PET imaging on measurement of disease progression and treatment effectiveness in antiangiogenic therapies. Based on the reviewed studies, multimodal imaging combining standard MRI with new physiological MRI techniques and metabolic PET imaging, in particular amino acid tracers, may have the ability to detect antiangiogenic drug susceptibility or resistance prior to morphological changes. As advances occur in the development of therapies that target specific biochemical or molecular pathways and alter tumor physiology in potentially predictable ways, the validation of physiological and metabolic imaging biomarkers will become increasingly important in the near future.}, subject = {Gliom}, language = {en} } @inproceedings{MendelRauberPalm, author = {Mendel, Robert and Rauber, David and Palm, Christoph}, title = {Exploring the Effects of Contrastive Learning on Homogeneous Medical Image Data}, series = {Bildverarbeitung f{\"u}r die Medizin 2023: Proceedings, German Workshop on Medical Image Computing, July 2- 4, 2023, Braunschweig}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2023: Proceedings, German Workshop on Medical Image Computing, July 2- 4, 2023, Braunschweig}, publisher = {Springer Vieweg}, address = {Wiesbaden}, doi = {10.1007/978-3-658-41657-7}, pages = {128 -- 13}, abstract = {We investigate contrastive learning in a multi-task learning setting classifying and segmenting early Barrett's cancer. How can contrastive learning be applied in a domain with few classes and low inter-class and inter-sample variance, potentially enabling image retrieval or image attribution? We introduce a data sampling strategy that mines per-lesion data for positive samples and keeps a queue of the recent projections as negative samples. We propose a masking strategy for the NT-Xent loss that keeps the negative set pure and removes samples from the same lesion. We show cohesion and uniqueness improvements of the proposed method in feature space. The introduction of the auxiliary objective does not affect the performance but adds the ability to indicate similarity between lesions. Therefore, the approach could enable downstream auto-documentation tasks on homogeneous medical image data.}, language = {en} } @misc{OPUS4-1458, title = {Bildverarbeitung f{\"u}r die Medizin 2021}, editor = {Palm, Christoph and Deserno, Thomas M. and Handels, Heinz and Maier, Andreas and Maier-Hein, Klaus H. and Tolxdorff, Thomas}, publisher = {Springer Vieweg}, address = {Wiesbdaden}, isbn = {978-3-658-33197-9}, issn = {1431-472X}, doi = {10.1007/978-3-658-33198-6}, pages = {361}, abstract = {In den letzten Jahren hat sich der Workshop "Bildverarbeitung f{\"u}r die Medizin" durch erfolgreiche Veranstaltungen etabliert. Ziel ist auch 2021 wieder die Darstellung aktueller Forschungsergebnisse und die Vertiefung der Gespr{\"a}che zwischen Wissenschaftlern, Industrie und Anwendern. Die Beitr{\"a}ge 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, Computerunterst{\"u}tzte Diagnose, Biomechanische Modellierung, Validierung und Qualit{\"a}tssicherung, Bildverarbeitung in der Telemedizin u.v.m.}, subject = {Bildanalyse}, language = {de} } @misc{MendelSouzaJrRauberetal., author = {Mendel, Robert and Souza Jr., Luis Antonio de and Rauber, David and Papa, Jo{\~a}o Paulo and Palm, Christoph}, title = {Abstract: Semi-supervised Segmentation Based on Error-correcting Supervision}, series = {Bildverarbeitung f{\"u}r die Medizin 2021. Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7-9, 2021}, journal = {Bildverarbeitung f{\"u}r die Medizin 2021. Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7-9, 2021}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-33197-9}, doi = {10.1007/978-3-658-33198-6_43}, pages = {178}, abstract = {Pixel-level classification is an essential part of computer vision. For learning from labeled data, many powerful deep learning models have been developed recently. In this work, we augment such supervised segmentation models by allowing them to learn from unlabeled data. Our semi-supervised approach, termed Error-Correcting Supervision, leverages a collaborative strategy. Apart from the supervised training on the labeled data, the segmentation network is judged by an additional network.}, subject = {Deep Learning}, language = {en} } @article{EbigboPalmMessmann, author = {Ebigbo, Alanna and Palm, Christoph and Messmann, Helmut}, title = {Barrett esophagus: What to expect from Artificial Intelligence?}, series = {Best Practice \& Research Clinical Gastroenterology}, volume = {52-53}, journal = {Best Practice \& Research Clinical Gastroenterology}, number = {June-August}, publisher = {Elsevier}, issn = {1521-6918}, doi = {10.1016/j.bpg.2021.101726}, abstract = {The evaluation and assessment of Barrett's esophagus is challenging for both expert and nonexpert endoscopists. However, the early diagnosis of cancer in Barrett's esophagus is crucial for its prognosis, and could save costs. Pre-clinical and clinical studies on the application of Artificial Intelligence (AI) in Barrett's esophagus have shown promising results. In this review, we focus on the current challenges and future perspectives of implementing AI systems in the management of patients with Barrett's esophagus.}, subject = {Deep Learning}, language = {en} } @article{MaierDesernoHandelsetal., author = {Maier, Andreas and Deserno, Thomas M. and Handels, Heinz and Maier-Hein, Klaus H. and Palm, Christoph and Tolxdorff, Thomas}, title = {Guest editorial of the IJCARS - BVM 2018 special issue}, series = {International Journal of Computer Assisted Radiology and Surgery}, volume = {14}, journal = {International Journal of Computer Assisted Radiology and Surgery}, publisher = {Springer}, doi = {10.1007/s11548-018-01902-0}, pages = {1 -- 2}, language = {en} } @article{PassosSouzaJrMendeletal., author = {Passos, Leandro A. and Souza Jr., Luis Antonio de and Mendel, Robert and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Palm, Christoph and Papa, Jo{\~a}o Paulo}, title = {Barrett's esophagus analysis using infinity Restricted Boltzmann Machines}, series = {Journal of Visual Communication and Image Representation}, volume = {59}, journal = {Journal of Visual Communication and Image Representation}, publisher = {Elsevier}, doi = {10.1016/j.jvcir.2019.01.043}, pages = {475 -- 485}, abstract = {The number of patients with Barret's esophagus (BE) has increased in the last decades. Considering the dangerousness of the disease and its evolution to adenocarcinoma, an early diagnosis of BE may provide a high probability of cancer remission. However, limitations regarding traditional methods of detection and management of BE demand alternative solutions. As such, computer-aided tools have been recently used to assist in this problem, but the challenge still persists. To manage the problem, we introduce the infinity Restricted Boltzmann Machines (iRBMs) to the task of automatic identification of Barrett's esophagus from endoscopic images of the lower esophagus. Moreover, since iRBM requires a proper selection of its meta-parameters, we also present a discriminative iRBM fine-tuning using six meta-heuristic optimization techniques. We showed that iRBMs are suitable for the context since it provides competitive results, as well as the meta-heuristic techniques showed to be appropriate for such task.}, subject = {Speiser{\"o}hrenkrankheit}, language = {en} } @inproceedings{SouzaJrAfonsoPalmetal., author = {Souza Jr., Luis Antonio de and Afonso, Luis Claudio Sugi and Palm, Christoph and Papa, Jo{\~a}o Paulo}, title = {Barrett's Esophagus Identification Using Optimum-Path Forest}, series = {Proceedings of the 30th Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T 2017), Niter{\´o}i, Rio de Janeiro, Brazil, 2017, 17-20 October}, booktitle = {Proceedings of the 30th Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T 2017), Niter{\´o}i, Rio de Janeiro, Brazil, 2017, 17-20 October}, doi = {10.1109/SIBGRAPI.2017.47}, pages = {308 -- 314}, abstract = {Computer-assisted analysis of endoscopic images can be helpful to the automatic diagnosis and classification of neoplastic lesions. Barrett's esophagus (BE) is a common type of reflux that is not straight forward to be detected by endoscopic surveillance, thus being way susceptible to erroneous diagnosis, which can cause cancer when not treated properly. In this work, we introduce the Optimum-Path Forest (OPF) classifier to the task of automatic identification of Barrett'sesophagus, with promising results and outperforming the well known Support Vector Machines (SVM) in the aforementioned context. We consider describing endoscopic images by means of feature extractors based on key point information, such as the Speeded up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT), for further designing a bag-of-visual-wordsthat is used to feed both OPF and SVM classifiers. The best results were obtained by means of the OPF classifier for both feature extractors, with values lying on 0.732 (SURF) - 0.735(SIFT) for sensitivity, 0.782 (SURF) - 0.806 (SIFT) for specificity, and 0.738 (SURF) - 0.732 (SIFT) for the accuracy.}, subject = {Speiser{\"o}hrenkrankheit}, language = {en} } @inproceedings{ZehnerSzaloPalm, author = {Zehner, Alexander and Szalo, Alexander Eduard and Palm, Christoph}, title = {GraphMIC: Easy Prototyping of Medical Image Computing Applications}, series = {Interactive Medical Image Computing (IMIC), Workshop at the Medical Image Computing and Computer Assisted Interventions (MICCAI 2015), 2015, Munich}, booktitle = {Interactive Medical Image Computing (IMIC), Workshop at the Medical Image Computing and Computer Assisted Interventions (MICCAI 2015), 2015, Munich}, doi = {10.13140/RG.2.1.3718.4725}, pages = {395 -- 400}, abstract = {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}, subject = {Bildverarbeitung}, language = {en} } @inproceedings{WeberDoenitzBrawanskietal., author = {Weber, Joachim and Doenitz, Christian and Brawanski, Alexander and Palm, Christoph}, title = {Data-Parallel MRI Brain Segmentation in Clinicial Use}, series = {Bildverarbeitung f{\"u}r die Medizin 2015; Algorithmen - Systeme - Anwendungen; Proceedings des Workshops vom 15. bis 17. M{\"a}rz 2015 in L{\"u}beck}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2015; Algorithmen - Systeme - Anwendungen; Proceedings des Workshops vom 15. bis 17. M{\"a}rz 2015 in L{\"u}beck}, publisher = {Springer}, address = {Berlin}, doi = {10.1007/978-3-662-46224-9_67}, pages = {389 -- 394}, abstract = {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.}, subject = {Kernspintomografie}, language = {en} } @misc{MaierWeihererHuberetal., author = {Maier, Johannes and Weiherer, Maximilian and Huber, Michaela and Palm, Christoph}, title = {Abstract: Imitating Human Soft Tissue with Dual-Material 3D Printing}, series = {Bildverarbeitung f{\"u}r die Medizin 2019, Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 17. bis 19. M{\"a}rz 2019 in L{\"u}beck}, journal = {Bildverarbeitung f{\"u}r die Medizin 2019, Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 17. bis 19. M{\"a}rz 2019 in L{\"u}beck}, editor = {Handels, Heinz and Deserno, Thomas M. and Maier, Andreas and Maier-Hein, Klaus H. and Palm, Christoph and Tolxdorff, Thomas}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-25325-7}, doi = {10.1007/978-3-658-25326-4_48}, pages = {218}, abstract = {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.}, subject = {Handchirurgie}, language = {en} } @misc{OPUS4-7115, title = {Bildverarbeitung f{\"u}r die Medizin 2024}, editor = {Maier, Andreas and Deserno, Thomas M. and Handels, Heinz and Maier-Hein, Klaus H. and Palm, Christoph and Tolxdorff, Thomas}, publisher = {Springer}, address = {Wiesbaden}, isbn = {978-3-658-44037-4}, issn = {1431-472X}, doi = {10.1007/978-3-658-44037-4}, pages = {370}, abstract = {Seit mehr als 25 Jahren ist der Workshop "Bildverarbeitung f{\"u}r die Medizin" als erfolgreiche Veranstaltung etabliert. Ziel ist auch 2024 wieder die Darstellung aktueller Forschungsergebnisse und die Vertiefung der Gespr{\"a}che zwischen Wissenschaftlern, Industrie und Anwendern. Die Beitr{\"a}ge dieses Bandes - viele davon in englischer Sprache - umfassen alle Bereiche der medizinischen Bildverarbeitung, insbesondere die Bildgebung und -akquisition, Segmentierung und Analyse, Visualisierung und Animation, computerunterst{\"u}tzte Diagnose sowie bildgest{\"u}tzte Therapieplanung und Therapie. Hierbei kommen Methoden des maschinelles Lernens, der biomechanischen Modellierung sowie der Validierung und Qualit{\"a}tssicherung zum Einsatz.}, subject = {Bildverarbeitung}, language = {de} } @misc{RueckertRiederRauberetal., author = {R{\"u}ckert, Tobias and Rieder, Maximilian and Rauber, David and Xiao, Michel and Humolli, Eg and Feussner, Hubertus and Wilhelm, Dirk and Palm, Christoph}, title = {Augmenting instrument segmentation in video sequences of minimally invasive surgery by synthetic smoky frames}, series = {International Journal of Computer Assisted Radiology and Surgery}, volume = {18}, journal = {International Journal of Computer Assisted Radiology and Surgery}, number = {Suppl 1}, publisher = {Springer Nature}, doi = {10.1007/s11548-023-02878-2}, pages = {S54 -- S56}, language = {en} } @article{MaerklRueckertRauberetal., author = {Maerkl, Raphaela and Rueckert, Tobias and Rauber, David and Gutbrod, Max and Weber Nunes, Danilo and Palm, Christoph}, title = {Enhancing generalization in zero-shot multi-label endoscopic instrument classification}, series = {International Journal of Computer Assisted Radiology and Surgery}, volume = {20}, journal = {International Journal of Computer Assisted Radiology and Surgery}, publisher = {Springer Nature}, doi = {10.1007/s11548-025-03439-5}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-85674}, pages = {1577 -- 1587}, abstract = {Purpose Recognizing previously unseen classes with neural networks is a significant challenge due to their limited generalization capabilities. This issue is particularly critical in safety-critical domains such as medical applications, where accurate classification is essential for reliability and patient safety. Zero-shot learning methods address this challenge by utilizing additional semantic data, with their performance relying heavily on the quality of the generated embeddings. Methods This work investigates the use of full descriptive sentences, generated by a Sentence-BERT model, as class representations, compared to simpler category-based word embeddings derived from a BERT model. Additionally, the impact of z-score normalization as a post-processing step on these embeddings is explored. The proposed approach is evaluated on a multi-label generalized zero-shot learning task, focusing on the recognition of surgical instruments in endoscopic images from minimally invasive cholecystectomies. Results The results demonstrate that combining sentence embeddings and z-score normalization significantly improves model performance. For unseen classes, the AUROC improves from 43.9\% to 64.9\%, and the multi-label accuracy from 26.1\% to 79.5\%. Overall performance measured across both seen and unseen classes improves from 49.3\% to 64.9\% in AUROC and from 37.3\% to 65.1\% in multi-label accuracy, highlighting the effectiveness of our approach. Conclusion These findings demonstrate that sentence embeddings and z-score normalization can substantially enhance the generalization performance of zero-shot learning models. However, as the study is based on a single dataset, future work should validate the method across diverse datasets and application domains to establish its robustness and broader applicability.}, language = {en} } @misc{ScheppachWeberNunesRauberetal., author = {Scheppach, Markus W. and Weber Nunes, Danilo and Rauber, David and Arizi, X. and Probst, Andreas and Nagl, Sandra and R{\"o}mmele, Christoph and Ebigbo, Alanna and Palm, Christoph and Messmann, Helmut}, title = {K{\"u}nstliche Intelligenz-basierte Erkennung von interventionellen Phasen bei der endoskopischen Submukosadissektion}, series = {Zeitschrift f{\"u}r Gastroenterologie}, volume = {63}, journal = {Zeitschrift f{\"u}r Gastroenterologie}, number = {08}, publisher = {Thieme}, address = {Stuttgart}, doi = {10.1055/s-0045-1811093}, pages = {e612 -- e613}, abstract = {Einleitung: Die endoskopische Submukosadissektion (ESD) ist ein komplexes endoskopisches Verfahren, das technische Expertise erfordert. Objektive Methoden zur Analyse von interventionellen Abl{\"a}ufen bei ESD k{\"o}nnten f{\"u}r Qualit{\"a}tssicherung und Ausbildung, wie auch eine automatische Befunderstellung von Nutzen sein. Ziele: In dieser Studie wurde ein KI-Algorithmus f{\"u}r die Erkennung und Klassifizierung der interventionellen Phasen der ESD entwickelt, um die technische Basis f{\"u}r eine standardisierte Leistungsbewertung und automatische Befunderstellung zu schaffen. Methodik: Vollst{\"a}ndige ESD-Videoaufnahmen von 49 Patienten wurden retrospektiv zusammengestellt. Der Datensatz umfasste 6.390.151 Einzelbilder, die alle f{\"u}r die folgenden interventionellen Phasen annotiert wurden: Diagnostik, Markierung, Injektion, Dissektion und H{\"a}mostase. 3.973.712 Bilder (28 Patienten) wurden f{\"u}r das Training eines Video-Swin-Transformers genutzt. Dabei wurde temporale Information durch standardisierte BIldextraktion in festgelegten zeitlichen Abst{\"a}nden zum analysierten Bild inkorporiert. 2.416.439 separate Bilder (21 Patienten) wurden f{\"u}r eine interne Validierung genutzt. Ergebnis: Bei der internen Evaluation erreichte das System insgesamt einen F1-Wert von 0,88. Es wurden F1-Werte von 0,99, 0,89, 0,89, 0,91 und 0,52 f{\"u}r Diagnostik, Markierung, Injektion, Dissektion bzw. Blutungsmanagement gemessen. Die Sensitivit{\"a}ten f{\"u}r dieselben Parameter betrugen 1,00, 0,80, 0,94, 0,89 und 0,67, die Spezifit{\"a}ten lagen bei 1,00, 1,00, 0,98, 0,88 und 0,93. Positive pr{\"a}diktive Werte wurden mit 0,98, 1,00, 0,85, 0,94 und 0,43 gemessen. Schlussfolgerung: In dieser vorl{\"a}ufigen Studie zeigte ein KI-Algorithmus eine hohe Leistungsf{\"a}higkeit f{\"u}r die Einzelbild-Erkennung von Verfahrensphasen w{\"a}hrend der ESD. Die vergleichsweise niedrige Leistung f{\"u}r die Blutungsphase wurde auf das seltene Auftreten von Blutungsepisoden im Trainingsdatensatz zur{\"u}ckgef{\"u}hrt, der zu diesem Zeitpunkt nur Videos in voller L{\"a}nge umfasste. Die zuk{\"u}nftige Entwicklung des Algorithmus wird sich auf die Reduzierung von Klassenungleichgewichten durch selektive Annotationsprotokolle konzentrieren.}, language = {de} } @misc{ScheppachRauberZingleretal., author = {Scheppach, Markus W. and Rauber, David and Zingler, C. and Weber Nunes, Danilo and Probst, Andreas and R{\"o}mmele, Christoph and Nagl, Sandra and Ebigbo, Alanna and Palm, Christoph and Messmann, Helmut}, title = {Instrumentenerkennung w{\"a}hrend der endoskopischen Submukosadissektion mittels k{\"u}nstlicher Intelligenz}, series = {Zeitschrift f{\"u}r Gastroenterologie}, volume = {63}, journal = {Zeitschrift f{\"u}r Gastroenterologie}, number = {8}, publisher = {Thieme}, doi = {10.1055/s-0045-1811092}, abstract = {Einleitung: Die endoskopische Submukosadissektion (ESD) ist eine komplexe Technik zur Resektion gastrointestinaler Fr{\"u}hneoplasien. Dabei werden f{\"u}r die verschiedenen Schritte der Intervention spezifische endoskopische Instrumente verwendet. Die pr{\"a}zise und automatische Erkennung und Abgrenzung der verwendeten Instrumente (Injektionsnadeln, elektrochirurgische Messer mit unterschiedlichen Konfigurationen, h{\"a}mostatische Zangen) k{\"o}nnte wertvolle Informationen {\"u}ber den Fortschritt und die Verfahrensmerkmale der ESD liefern und eine automatische standardisierte Berichterstattung erm{\"o}glichen. 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×{\"o}sophageal, 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 h{\"a}mostatische 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 Sensitivit{\"a}t von 0,81 und eine Spezifit{\"a}t 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 f{\"u}r die Klassen Hakenmesser - Katheter und - Spitze, Nadelmesser - Katheter und - Spitze, Injektionsnadel - Katheter und - Spitze, h{\"a}mostatische Zange - Katheter und - Spitze gemessen. Schlussfolgerung: In dieser Studie wurden die wichtigsten endoskopischen Instrumente, die w{\"a}hrend der ESD verwendet werden, mit hoher Genauigkeit erkannt. Die geringere Leistung bei der h{\"a}mostatische Zange - Katheter kann auf die Unterrepr{\"a}sentation dieser Klassen in den Trainingsdaten zur{\"u}ckgef{\"u}hrt werden. Zuk{\"u}nftige Studien werden sich auf die Erweiterung der Instrumentenklassen sowie auf die Ausbalancierung der Trainingsdaten konzentrieren.}, language = {de} } @inproceedings{KlausmannRueckertRauberetal., author = {Klausmann, Leonard and Rueckert, Tobias and Rauber, David and Maerkl, Raphaela and Yildiran, Suemeyye R. and Gutbrod, Max and Palm, Christoph}, title = {DIY challenge blueprint: from organization to technical realization in biomedical image analysis}, series = {Medical Image Computing and Computer Assisted Intervention - MICCAI 2025 ; Proceedings Part XI}, booktitle = {Medical Image Computing and Computer Assisted Intervention - MICCAI 2025 ; Proceedings Part XI}, publisher = {Springer}, address = {Cham}, isbn = {978-3-032-05141-7}, doi = {10.1007/978-3-032-05141-7_9}, pages = {85 -- 95}, abstract = {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.}, language = {en} } @unpublished{GutbrodRauberWeberNunesetal., author = {Gutbrod, Max and Rauber, David and Weber Nunes, Danilo and Palm, Christoph}, title = {OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution Detection}, doi = {10.48550/arXiv.2503.16247}, pages = {18}, abstract = {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.}, language = {en} } @article{WeihererEigenbergerEggeretal., author = {Weiherer, Maximilian and Eigenberger, Andreas and Egger, Bernhard and Br{\´e}bant, Vanessa and Prantl, Lukas and Palm, Christoph}, title = {Learning the shape of female breasts: an open-access 3D statistical shape model of the female breast built from 110 breast scans}, series = {The Visual Computer}, volume = {39}, journal = {The Visual Computer}, number = {4}, publisher = {Springer Nature}, doi = {10.1007/s00371-022-02431-3}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-30506}, pages = {1597 -- 1616}, abstract = {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/.}, language = {en} } @article{BrebantWeihererNoisseretal., author = {Br{\´e}bant, Vanessa and Weiherer, Maximilian and Noisser, Vivien and Seitz, Stephan and Prantl, Lukas and Eigenberger, Andreas}, title = {Implants Versus Lipograft: Analysis of Long-Term Results Following Congenital Breast Asymmetry Correction}, series = {Aesthetic Plastic Surgery}, volume = {46}, journal = {Aesthetic Plastic Surgery}, publisher = {Springer Nature}, doi = {10.1007/s00266-022-02843-5}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-32404}, pages = {2228 -- 2236}, abstract = {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.}, language = {en} } @article{MaierWeihererHuberetal., author = {Maier, Johannes and Weiherer, Maximilian and Huber, Michaela and Palm, Christoph}, title = {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}, series = {Quantitative Imaging in Medicine and Surgery}, volume = {9}, journal = {Quantitative Imaging in Medicine and Surgery}, number = {1}, publisher = {AME Publishing Company}, doi = {10.21037/qims.2018.09.17}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-979}, pages = {30 -- 42}, abstract = {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.}, subject = {Handchirurgie}, language = {en} } @article{MaierWeihererHuberetal., author = {Maier, Johannes and Weiherer, Maximilian and Huber, Michaela and Palm, Christoph}, title = {Optically tracked and 3D printed haptic phantom hand for surgical training system}, series = {Quantitative Imaging in Medicine and Surgery}, volume = {10}, journal = {Quantitative Imaging in Medicine and Surgery}, number = {02}, publisher = {AME Publishing Company}, address = {Hong Kong, China}, doi = {10.21037/qims.2019.12.03}, pages = {340 -- 455}, abstract = {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.}, subject = {Handchirurgie}, language = {en} } @article{NoisserEigenbergerWeihereretal., author = {Noisser, Vivien and Eigenberger, Andreas and Weiherer, Maximilian and Seitz, Stephan and Prantl, Lukas and Br{\´e}bant, Vanessa}, title = {Surgery of congenital breast asymmetry - which objective parameter influences the subjective satisfaction with long-term results}, series = {Archives of Gynecology and Obstetrics}, journal = {Archives of Gynecology and Obstetrics}, publisher = {Springer Nature}, doi = {10.1007/s00404-021-06218-0}, pages = {8}, abstract = {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.}, language = {en} }