@article{WoehlMaierGehmertetal., author = {W{\"o}hl, Rebecca and Maier, Johannes and Gehmert, Sebastian and Palm, Christoph and Riebschl{\"a}ger, Birgit and Nerlich, Michael and Huber, Michaela}, title = {3D Analysis of Osteosyntheses Material using semi-automated CT Segmentation}, series = {BMC Musculoskeletal Disorders}, volume = {19}, journal = {BMC Musculoskeletal Disorders}, publisher = {Springer Nature}, doi = {10.1186/s12891-018-1975-0}, pages = {1 -- 8}, abstract = {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.}, subject = {Handchirurgie}, language = {en} } @misc{ScheppachMendelProbstetal., author = {Scheppach, Markus W. and Mendel, Robert and Probst, Andreas and Rauber, David and R{\"u}ckert, Tobias and Meinikheim, Michael and Palm, Christoph and Messmann, Helmut and Ebigbo, Alanna}, title = {Real-time detection and delineation of tissue during third-space endoscopy using artificial intelligence (AI)}, series = {Endoscopy}, volume = {55}, journal = {Endoscopy}, number = {S02}, publisher = {Thieme}, doi = {10.1055/s-0043-1765128}, pages = {S53 -- S54}, abstract = {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.}, subject = {Speiser{\"o}hrenkrankheit}, language = {en} } @inproceedings{FranzKatzkyNeumannetal., author = {Franz, Daniela and Katzky, Uwe and Neumann, Sabine and Perret, Jerome and Hofer, Mathias and Huber, Michaela and Schmitt-R{\"u}th, Stephanie and Haug, Sonja and Weber, Karsten and Prinzen, Martin and Palm, Christoph and Wittenberg, Thomas}, title = {Haptisches Lernen f{\"u}r Cochlea Implantationen}, series = {15. Jahrestagung der Deutschen Gesellschaft f{\"u}r Computer- und Roboterassistierte Chirurgie (CURAC2016), Tagungsband, 2016, Bern, 29.09. - 01.10.}, booktitle = {15. Jahrestagung der Deutschen Gesellschaft f{\"u}r Computer- und Roboterassistierte Chirurgie (CURAC2016), Tagungsband, 2016, Bern, 29.09. - 01.10.}, pages = {21 -- 26}, abstract = {Die Implantation eines Cochlea Implantates ben{\"o}tigt einen chirurgischen Zugang im Felsenbein und durch die Paukenh{\"o}hle des Patienten. Der Chirurg hat eine eingeschr{\"a}nkte Sicht im Operationsgebiet, die weiterhin viele Risikostrukturen enth{\"a}lt. Um eine Cochlea Implantation sicher und fehlerfrei durchzuf{\"u}hren, ist eine umfangreiche theoretische und praktische (teilweise berufsbegleitende) Fortbildung sowie langj{\"a}hrige Erfahrung notwendig. Unter Nutzung von realen klinischen CT/MRT Daten von Innen- und Mittelohr und der interaktiven Segmentierung der darin abgebildeten Strukturen (Nerven, Cochlea, Geh{\"o}rkn{\"o}chelchen,...) wird im HaptiVisT Projekt ein haptisch-visuelles Trainingssystem f{\"u}r die Implantation von Innen- und Mittelohr-Implantaten realisiert, das als sog. „Serious Game" mit immersiver Didaktik gestaltet wird. Die Evaluierung des Demonstrators hinsichtlich Zweckm{\"a}ßigkeit erfolgt prozessbegleitend und ergebnisorientiert, um m{\"o}gliche technische oder didaktische Fehler vor Fertigstellung des Systems aufzudecken. Drei zeitlich versetzte Evaluationen fokussieren dabei chirurgisch-fachliche, didaktische sowie haptisch-ergonomische Akzeptanzkriterien.}, subject = {Cochlea-Implantat}, language = {de} } @inproceedings{SouzaJrHookPapaetal., author = {Souza Jr., Luis Antonio de and Hook, Christian and Papa, Jo{\~a}o Paulo and Palm, Christoph}, title = {Barrett's Esophagus Analysis Using SURF Features}, series = {Bildverarbeitung f{\"u}r die Medizin 2017; Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 12. bis 14. M{\"a}rz 2017 in Heidelberg}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2017; Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 12. bis 14. M{\"a}rz 2017 in Heidelberg}, publisher = {Springer}, address = {Berlin}, doi = {10.1007/978-3-662-54345-0_34}, pages = {141 -- 146}, abstract = {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.}, subject = {Speiser{\"o}hrenkrankheit}, language = {en} } @inproceedings{MendelEbigboProbstetal., author = {Mendel, Robert and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Palm, Christoph}, title = {Barrett's Esophagus Analysis Using Convolutional Neural Networks}, series = {Bildverarbeitung f{\"u}r die Medizin 2017; Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 12. bis 14. M{\"a}rz 2017 in Heidelberg}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2017; Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 12. bis 14. M{\"a}rz 2017 in Heidelberg}, publisher = {Springer}, address = {Berlin}, doi = {10.1007/978-3-662-54345-0_23}, pages = {80 -- 85}, abstract = {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.}, subject = {Speiser{\"o}hrenkrebs}, language = {en} } @inproceedings{MaierHaugHuberetal., author = {Maier, Johannes and Haug, Sonja and Huber, Michaela and Katzky, Uwe and Neumann, Sabine and Perret, J{\´e}r{\^o}me and Prinzen, Martin and Weber, Karsten and Wittenberg, Thomas and W{\"o}hl, Rebecca and Scorna, Ulrike and Palm, Christoph}, title = {Development of a haptic and visual assisted training simulation concept for complex bone drilling in minimally invasive hand surgery}, series = {CARS Conference, 5.10.-7.10.2017}, booktitle = {CARS Conference, 5.10.-7.10.2017}, language = {en} } @article{SouzaJrPassosSantanaetal., author = {Souza Jr., Luis Antonio de and Passos, Leandro A. and Santana, Marcos Cleison S. and Mendel, Robert and Rauber, David and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Papa, Jo{\~a}o Paulo and Palm, Christoph}, title = {Layer-selective deep representation to improve esophageal cancer classification}, series = {Medical \& Biological Engineering \& Computing}, volume = {62}, journal = {Medical \& Biological Engineering \& Computing}, publisher = {Springer Nature}, address = {Heidelberg}, doi = {10.1007/s11517-024-03142-8}, pages = {3355 -- 3372}, abstract = {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.}, language = {en} } @article{EbigboMendelProbstetal., author = {Ebigbo, Alanna and Mendel, Robert and Probst, Andreas and Meinikheim, Michael and Byrne, Michael F. and Messmann, Helmut and Palm, Christoph}, title = {Multimodal imaging for detection and segmentation of Barrett's esophagus-related neoplasia using artificial intelligence}, series = {Endoscopy}, volume = {54}, journal = {Endoscopy}, number = {10}, edition = {E-Video}, publisher = {Georg Thieme Verlag}, address = {Stuttgart}, doi = {10.1055/a-1704-7885}, pages = {1}, abstract = {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.}, language = {en} } @article{KolevKirchgessnerHoubenetal., author = {Kolev, Kalin and Kirchgeßner, Norbert and Houben, Sebastian and Csisz{\´a}r, Agnes and Rubner, Wolfgang and Palm, Christoph and Eiben, Bj{\"o}rn and Merkel, Rudolf and Cremers, Daniel}, title = {A variational approach to vesicle membrane reconstruction from fluorescence imaging}, series = {Pattern Recognition}, volume = {44}, journal = {Pattern Recognition}, number = {12}, publisher = {Elsevier}, doi = {10.1016/j.patcog.2011.04.019}, pages = {2944 -- 2958}, abstract = {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.}, subject = {Dreidimensionale Bildverarbeitung}, language = {en} } @inproceedings{MetzlerAachPalmetal., author = {Metzler, V. and Aach, T. and Palm, Christoph and Lehmann, Thomas M.}, title = {Texture Classification of Graylevel Images by Multiscale Cross-Co-Occurrence Matrices}, series = {Proceedings 15th International Conference on Pattern Recognition (ICPR-2000)}, booktitle = {Proceedings 15th International Conference on Pattern Recognition (ICPR-2000)}, doi = {10.1109/ICPR.2000.906133}, pages = {549 -- 552}, abstract = {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.}, language = {en} } @misc{OPUS4-100, title = {Bildverarbeitung f{\"u}r die Medizin 2019}, editor = {Handels, Heinz and Deserno, Thomas M. and Maier, Andreas and Maier-Hein, Klaus H. and Palm, Christoph and Tolxdorff, Thomas}, publisher = {Springer}, address = {Berlin}, isbn = {978-3-658-25325-7}, issn = {1431-472X}, doi = {10.1007/978-3-658-25326-4}, abstract = {In den letzten Jahren hat sich der Workshop "Bildverarbeitung f{\"u}r die Medizin" durch erfolgreiche Veranstaltungen etabliert. Ziel ist auch 2019 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 = {Bildgebendes Verfahren}, language = {de} } @misc{OPUS4-349, title = {Bildverarbeitung f{\"u}r die Medizin 2020}, editor = {Tolxdorff, Thomas and Deserno, Thomas M. and Handels, Heinz and Maier, Andreas and Maier-Hein, Klaus H. and Palm, Christoph}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-29266-9}, doi = {10.1007/978-3-658-29267-6}, abstract = {In den letzten Jahren hat sich der Workshop "Bildverarbeitung f{\"u}r die Medizin" durch erfolgreiche Veranstaltungen etabliert. Ziel ist auch 2020 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{MeinikheimMendelProbstetal., author = {Meinikheim, Michael and Mendel, Robert and Probst, Andreas and Scheppach, Markus W. and Messmann, Helmut and Palm, Christoph and Ebigbo, Alanna}, title = {Optical Flow als Methode zur Qualit{\"a}tssicherung KI-unterst{\"u}tzter Untersuchungen von Barrett-{\"O}sophagus und Barrett-{\"O}sophagus assoziierten Neoplasien}, series = {Zeitschrift f{\"u}r Gastroenterologie}, volume = {60}, journal = {Zeitschrift f{\"u}r Gastroenterologie}, number = {08}, publisher = {Georg Thieme Verlag}, address = {Stuttgart}, doi = {10.1055/s-0042-1754997}, abstract = {Einleitung {\"U}berm{\"a}ßige Bewegung im Bild kann die Performance von auf k{\"u}nstlicher Intelligenz (KI) basierenden klinischen Entscheidungsunterst{\"u}tzungssystemen (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 Unterst{\"u}tzung bei der Beurteilung von Barrett-{\"O}sophagus (BE) verwenden, ein Echtzeit-Feedback zur aktuellen Datenqualit{\"a}t anzubieten. Methodik Dazu wurden unver{\"a}nderte 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-{\"O}sophagus assoziierten Neoplasien (BERN) durch unseren KI-Algorithmus analysiert. Der zur Bewertung der Bildqualit{\"a}t verwendete OF beinhaltete die mittlere Magnitude und die Entropie des Histogramms der Winkel. Frames wurden automatisch extrahiert, wenn die vordefinierten Schwellenwerte von 3,0 f{\"u}r die mittlere Magnitude und 9,0 f{\"u}r die Entropie des Histogramms der Winkel {\"u}berschritten wurden. Experten sahen sich zun{\"a}chst die Videos ohne KI-Unterst{\"u}tzung an und bewerteten, ob St{\"o}rfaktoren die Sicherheit mit der eine Diagnose im vorliegenden Fall gestellt werden kann negativ beeinflussen. Anschließend {\"u}berpr{\"u}ften sie die extrahierten Frames. Ergebnis Gleichm{\"a}ßige Bewegung in eine Richtung, wie etwa beim Vorschieben des Endoskops, spiegelte sich, bei insignifikant ver{\"a}nderter Entropie, in einer Erh{\"o}hung der Magnitude wider. Chaotische Bewegung, zum Beispiel w{\"a}hrend dem Sp{\"u}len, war mit erh{\"o}hter Entropie assoziiert. Insgesamt war eine unruhige endoskopische Darstellung, Fl{\"u}ssigkeit sowie {\"u}berm{\"a}ßige {\"O}sophagusmotilit{\"a}t mit erh{\"o}htem OF assoziiert und korrelierte mit der Meinung der Experten {\"u}ber die Qualit{\"a}t der Videos. Der OF und die subjektive Wahrnehmung der Experten {\"u}ber die Verwertbarkeit der vorliegenden Bildsequenzen korrelierten direkt proportional. Wenn die vordefinierten Schwellenwerte des OF {\"u}berschritten wurden, war die damit verbundene Bildqualit{\"a}t in 94\% der F{\"a}lle f{\"u}r eine definitive Interpretation auch f{\"u}r Experten unzureichend. Schlussfolgerung OF hat das Potenzial Endoskopiker ein Echtzeit-Feedback {\"u}ber die Qualit{\"a}t des Dateninputs zu bieten und so nicht nur die HCI zu verbessern, sondern auch die optimale Performance von KI-Algorithmen zu erm{\"o}glichen.}, language = {de} } @misc{MeinikheimMendelScheppachetal., author = {Meinikheim, Michael and Mendel, Robert and Scheppach, Markus W. and Probst, Andreas and Prinz, Friederike and Schwamberger, Tanja and Schlottmann, Jakob and G{\"o}lder, Stefan Karl and Walter, Benjamin and Steinbr{\"u}ck, Ingo and Palm, Christoph and Messmann, Helmut and Ebigbo, Alanna}, title = {Einsatz von k{\"u}nstlicher Intelligenz (KI) als Entscheidungsunterst{\"u}tzungssystem f{\"u}r nicht-Experten bei der Beurteilung von Barrett-{\"O}sophagus assoziierten Neoplasien (BERN)}, series = {Zeitschrift f{\"u}r Gastroenterologie}, volume = {60}, journal = {Zeitschrift f{\"u}r Gastroenterologie}, number = {4}, publisher = {Thieme}, address = {Stuttgart}, doi = {10.1055/s-0042-1745653}, pages = {251}, abstract = {Einleitung Die sichere Detektion und Charakterisierung von Barrett-{\"O}sophagus assoziierten Neoplasien (BERN) stellt selbst f{\"u}r erfahrene Endoskopiker eine Herausforderung dar. Ziel Ziel dieser Studie ist es, den Add-on Effekt eines k{\"u}nstlichen Intelligenz (KI) Systems (Barrett-Ampel) als Entscheidungsunterst{\"u}zungssystem f{\"u}r Endoskopiker ohne Expertise bei der Untersuchung von BERN zu evaluieren. Material und Methodik Zw{\"o}lf Videos in „Weißlicht" (WL), „narrow-band imaging" (NBI) und „texture and color enhanced imaging" (TXI) von histologisch best{\"a}tigten 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 zus{\"a}tzlicher KI-Unterst{\"u}tzung gezeigt. Basierend auf dieser neuen Information, wurden die Probanden zu einer Reevaluation ihrer initialen Beurteilung aufgefordert. Ergebnisse Die „Barrett-Ampel" identifizierte unabh{\"a}ngig von den verwendeten Darstellungsmodi (WL, NBI, TXI) alle BERN. Zwei entz{\"u}ndlich ver{\"a}nderte L{\"a}sionen wurden fehlinterpretiert (Genauigkeit=75\%). W{\"a}hrend 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 unterst{\"u}tzt, erreichten diese eine Genauigkeit von 75\%. Zusammenfassung Unser KI-System hat das Potential als Entscheidungsunterst{\"u}tzungssystem 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 best{\"a}tigen, m{\"u}ssen randomisierte kontrollierte klinische Studien durchgef{\"u}hrt werden.}, language = {de} } @misc{MeinikheimMendelScheppachetal., author = {Meinikheim, Michael and Mendel, Robert and Scheppach, Markus W. and Probst, Andreas and Prinz, Friederike and Schwamberger, Tanja and Schlottmann, Jakob and G{\"o}lder, Stefan Karl and Walter, Benjamin and Steinbr{\"u}ck, Ingo and Palm, Christoph and Messmann, Helmut and Ebigbo, Alanna}, title = {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)}, series = {Endoscopy}, volume = {54}, journal = {Endoscopy}, number = {S 01}, publisher = {Thieme}, doi = {10.1055/s-00000012}, pages = {S39}, abstract = {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.}, subject = {Speiser{\"o}hrenkrankheit}, language = {en} } @article{ScheppachMendelProbstetal., author = {Scheppach, Markus W. and Mendel, Robert and Probst, Andreas and Meinikheim, Michael and Palm, Christoph and Messmann, Helmut and Ebigbo, Alanna}, title = {ARTIFICIAL INTELLIGENCE (AI) - ASSISTED VESSEL AND TISSUE RECOGNITION IN THIRD-SPACE ENDOSCOPY}, series = {Endoscopy}, volume = {54}, journal = {Endoscopy}, number = {S01}, publisher = {Thieme}, doi = {10.1055/s-0042-1745037}, pages = {S175}, abstract = {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.}, language = {en} } @inproceedings{WeberNunesRauberPalm, author = {Weber Nunes, Danilo and Rauber, David and Palm, Christoph}, title = {Self-supervised 3D Vision Transformer Pre-training for Robust Brain Tumor Classification}, series = {Bildverarbeitung f{\"u}r die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Regensburg March 09-11, 2025}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Regensburg March 09-11, 2025}, editor = {Palm, Christoph and Breininger, Katharina and Deserno, Thomas M. and Handels, Heinz and Maier, Andreas and Maier-Hein, Klaus H. and Tolxdorff, Thomas}, publisher = {Springer Vieweg}, address = {Wiesbaden}, doi = {10.1007/978-3-658-47422-5_69}, pages = {298 -- 303}, abstract = {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.}, language = {en} } @inproceedings{WeiherervonRiedheimBrebantetal., author = {Weiherer, Maximilian and von Riedheim, Antonia and Br{\´e}bant, Vanessa and Egger, Bernhard and Palm, Christoph}, title = {iRBSM: A Deep Implicit 3D Breast Shape Model}, series = {Bildverarbeitung f{\"u}r die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Regensburg March 09-11, 2025}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Regensburg March 09-11, 2025}, editor = {Palm, Christoph and Breininger, Katharina and Deserno, Thomas M. and Handels, Heinz and Maier, Andreas and Maier-Hein, Klaus H. and Tolxdorff, Thomas}, publisher = {Springer Vieweg}, address = {Wiesbaden}, doi = {10.1007/978-3-658-47422-5_11}, pages = {38 -- 43}, abstract = {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.}, language = {en} } @misc{OPUS4-6079, title = {Bildverarbeitung f{\"u}r die Medizin 2023}, editor = {Deserno, Thomas M. and Handels, Heinz and Maier, Andreas and Maier-Hein, Klaus H. and Palm, Christoph and Tolxdorff, Thomas}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-41656-0}, issn = {1431-472X}, doi = {10.1007/978-3-658-41657-7}, pages = {317}, abstract = {Seit mehr als 25 Jahren ist der Workshop "Bildverarbeitung f{\"u}r die Medizin" als erfolgreiche Veranstaltung etabliert. Ziel ist auch 2023 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} } @inproceedings{GutbrodGeislerRauberetal., author = {Gutbrod, Max and Geisler, Benedikt and Rauber, David and Palm, Christoph}, title = {Data Augmentation for Images of Chronic Foot Wounds}, series = {Bildverarbeitung f{\"u}r die Medizin 2024: Proceedings, German Workshop on Medical Image Computing, March 10-12, 2024, Erlangen}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2024: Proceedings, German Workshop on Medical Image Computing, March 10-12, 2024, Erlangen}, 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}, doi = {10.1007/978-3-658-44037-4_71}, pages = {261 -- 266}, abstract = {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.}, language = {en} } @misc{SchroederSemmelmannSiegmundetal., author = {Schroeder, Josef A. and Semmelmann, Matthias and Siegmund, Heiko and Grafe, Claudia and Evert, Matthias and Palm, Christoph}, title = {Improved interactive computer-assisted approach for evaluation of ultrastructural cilia abnormalities}, series = {Ultrastructural Pathology}, volume = {41}, journal = {Ultrastructural Pathology}, number = {1}, doi = {10.1080/01913123.2016.1270978}, pages = {112 -- 113}, subject = {Zilie}, language = {en} } @misc{OPUS4-106, title = {Bildverarbeitung f{\"u}r die Medizin 2018}, editor = {Maier, Andreas and Deserno, Thomas M. and Handels, Heinz and Maier-Hein, Klaus H. and Palm, Christoph and Tolxdorff, Thomas}, publisher = {Springer}, address = {Berlin}, doi = {10.1007/978-3-662-56537-7}, subject = {Bildanalyse}, language = {de} } @misc{OPUS4-7967, title = {Bildverarbeitung f{\"u}r die Medizin 2025}, editor = {Palm, Christoph and Breininger, Katharina and Deserno, Thomas M. and Handels, Heinz and Maier, Andreas and Maier-Hein, Klaus H. and Tolxdorff, Thomas M.}, publisher = {Springer Fachmedien Wiesbaden}, address = {Wiesbaden}, isbn = {978-3-658-47421-8}, issn = {1431-472X}, doi = {10.1007/978-3-658-47422-5}, pages = {XXIII, 354}, abstract = {Die Konferenz "BVM - Bildverarbeitung f{\"u}r die Medizin" ist seit vielen Jahren als die nationale Plattform f{\"u}r den Austausch von Ideen und die Diskussion der neuesten Forschungsergebnisse im Bereich der Medizinischen Bildverarbeitung und der K{\"u}nstlichen Intelligenz (KI) etabliert. Auch 2025 werden wir aktuelle Forschungsergebnisse vorstellen und Gespr{\"a}che zwischen (jungen) Wissenschaftler*innen, Industrie und Anwender*innen vertiefen. Die Beitr{\"a}ge 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, computerunterst{\"u}tzte Diagnose sowie bildgest{\"u}tzte Therapieplanung und Therapie. Hierbei kommen Methoden des maschinellen Lernens, der biomechanischen Modellierung sowie der Validierung und Qualit{\"a}tssicherung 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 {\"u}ber link.springer.com frei verf{\"u}gbar (Open Access). Die Herausgebenden Prof. Palm forscht im Bereich KI f{\"u}r die Medizin mit einem Schwerpunkt in der Analyse endoskopischer Bilddaten zur computerunterst{\"u}tzten Diagnose und Therapie. Prof. Breininger entwickelt robuste Ans{\"a}tze des maschinellen Lernens in verschiedenen interdisziplin{\"a}ren 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, lernf{\"a}hige Bildverarbeitungsmethoden und integriert diese in hybride Bildverarbeitungssysteme zur Unterst{\"u}tzung der medizinischen Diagnostik und Therapie. Prof. Maier entwickelt Anwendungen in der medizinischen Bildverarbeitung zur Diagnoseunterst{\"u}tzung bis hin zur Schichtbildberechnung durch k{\"u}nstliche Intelligenz. Prof. Maier-Hein forscht im Bereich maschinelles Lernen und entwickelt Open-Source-L{\"o}sungen wie das Medical Imaging Interaction Toolkit (MITK), Kaapana oder das nnU-Net. Prof. em. Tolxdorff ist Experte f{\"u}r maschinelles Lernen, biomedizinisches Datenmanagement, Datenvisualisierung und -analyse sowie Medizinproduktentwicklung in klinischen Workflows.}, subject = {Bildverarbeitung}, language = {de} } @article{HartwigBerletCzempieletal., author = {Hartwig, Regine and Berlet, Maximilian and Czempiel, Tobias and Fuchtmann, Jonas and R{\"u}ckert, Tobias and Feussner, Hubertus and Wilhelm, Dirk}, title = {Bildbasierte Unterst{\"u}tzungsmethoden f{\"u}r die zuk{\"u}nftige Anwendung in der Chirurgie}, series = {Die Chirurgie}, volume = {93}, journal = {Die Chirurgie}, publisher = {Springer}, doi = {10.1007/s00104-022-01668-x}, pages = {956 -- 965}, abstract = {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. Hierf{\"u}r k{\"o}nnen unterschiedliche Sensorsysteme genutzt werden, wobei videobasierte Systeme ein besonderes Potenzial aufweisen. Methode: Anhand von Literaturangaben und auf Basis eigener Forschungsarbeiten werden zentrale Aspekte bildbasierter Unterst{\"u}tzungssysteme f{\"u}r die Chirurgie dargestellt. Hierbei wird deren Potenzial, aber auch die Limitationen der Methoden erl{\"a}utert. Ergebnisse: Eine etablierte Anwendung stellt die Phasendetektion chirurgischer Eingriffe dar, f{\"u}r die Operationsvideos mittels neuronaler Netzwerke analysiert werden. Durch eine zeitlich gest{\"u}tzte und transformative Analyse konnten die Ergebnisse der Pr{\"a}diktion j{\"u}ngst deutlich verbessert werden. Aber auch robotische Kameraf{\"u}hrungssysteme nutzen Bilddaten, um das Laparoskop zuk{\"u}nftig autonom zu navigieren. Um die Zuverl{\"a}ssigkeit an die hohen Anforderungen in der Chirurgie anzugleichen, m{\"u}ssen diese jedoch durch zus{\"a}tzliche Informationen erg{\"a}nzt werden. Ein vergleichbarer multimodaler Ansatz wurde bereits f{\"u}r die Navigation und Lokalisation bei laparoskopischen Eingriffen umgesetzt. Hierzu werden Videodaten mittels verschiedener Methoden analysiert und diese Ergebnisse mit anderen Sensormodalit{\"a}ten fusioniert. Diskussion: Bildbasierte Unterst{\"u}tzungsmethoden sind bereits f{\"u}r diverse Aufgaben verf{\"u}gbar und stellen einen wichtigen Aspekt f{\"u}r die Chirurgie der Zukunft dar. Um hier jedoch zuverl{\"a}ssig und f{\"u}r autonome Funktionen eingesetzt werden zu k{\"o}nnen, m{\"u}ssen sie zuk{\"u}nftig in multimodale Ans{\"a}tze eingebettet werden, um die erforderliche Sicherheit bieten zu k{\"o}nnen.}, language = {de} } @inproceedings{RueckertRiederFeussneretal., author = {R{\"u}ckert, Tobias and Rieder, Maximilian and Feussner, Hubertus and Wilhelm, Dirk and R{\"u}ckert, Daniel and Palm, Christoph}, title = {Smoke Classification in Laparoscopic Cholecystectomy Videos Incorporating Spatio-temporal Information}, series = {Bildverarbeitung f{\"u}r die Medizin 2024: Proceedings, German Workshop on Medical Image Computing, March 10-12, 2024, Erlangen}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2024: Proceedings, German Workshop on Medical Image Computing, March 10-12, 2024, Erlangen}, editor = {Maier, Andreas and Deserno, Thomas M. and Handels, Heinz and Maier-Hein, Klaus H. and Palm, Christoph and Tolxdorff, Thomas}, publisher = {Springeer}, address = {Wiesbaden}, doi = {10.1007/978-3-658-44037-4_78}, pages = {298 -- 303}, abstract = {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.}, language = {en} } @unpublished{MendelRueckertWilhelmetal., author = {Mendel, Robert and R{\"u}ckert, Tobias and Wilhelm, Dirk and R{\"u}ckert, Daniel and Palm, Christoph}, title = {Motion-Corrected Moving Average: Including Post-Hoc Temporal Information for Improved Video Segmentation}, doi = {10.48550/arXiv.2403.03120}, pages = {9}, abstract = {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.}, subject = {Deep Learning}, language = {en} }