TY - JOUR A1 - Scharfenberg, Georg A1 - Mottok, Jürgen A1 - Artmann, Christina A1 - Hobelsberger, Martin A1 - Paric, Ivan A1 - Großmann, Benjamin A1 - Pohlt, Clemens A1 - Wackerbarth, Alena A1 - Pausch, Uli A1 - Heidrich, Christiane A1 - Fadanelli, Martin A1 - Elsner, Michael A1 - Pöcher, Daniel A1 - Pittroff, Lenz A1 - Beer, Stefan A1 - Brückl, Oliver A1 - Haslbeck, Matthias A1 - Sterner, Michael A1 - Thema, Martin A1 - Muggenthaler, Nicole A1 - Lenck, Thorsten A1 - Götz, Philipp A1 - Eckert, Fabian A1 - Deubzer, Michael A1 - Stingl, Armin A1 - Simsek, Erol A1 - Krämer, Stefan A1 - Großmann, Benjamin A1 - Schlegl, Thomas A1 - Niedersteiner, Sascha A1 - Berlehner, Thomas A1 - Joblin, Mitchell A1 - Mauerer, Wolfgang A1 - Apel, Sven A1 - Siegmund, Janet A1 - Riehle, Dirk A1 - Weber, Joachim A1 - Palm, Christoph A1 - Zobel, Martin A1 - Al-Falouji, Ghassan A1 - Prestel, Dietmar A1 - Scharfenberg, Georg A1 - Mandl, Roland A1 - Deinzer, Arnulf A1 - Halang, W. A1 - Margraf-Stiksrud, Jutta A1 - Sick, Bernhard A1 - Deinzer, Renate A1 - Scherzinger, Stefanie A1 - Klettke, Meike A1 - Störl, Uta A1 - Wiech, Katharina A1 - Kubata, Christoph A1 - Sindersberger, Dirk A1 - Monkman, Gareth J. A1 - Dollinger, Markus A1 - Dembianny, Sven A1 - Kölbl, Andreas A1 - Welker, Franz A1 - Meier, Matthias A1 - Thumann, Philipp A1 - Swidergal, Krzysztof A1 - Wagner, Marcus A1 - Haug, Sonja A1 - Vernim, Matthias A1 - Seidenstücker, Barbara A1 - Weber, Karsten A1 - Arsan, Christian A1 - Schone, Reinhold A1 - Münder, Johannes A1 - Schroll-Decker, Irmgard A1 - Dillinger, Andrea Elisabeth A1 - Fuchshofer, Rudolf A1 - Monkman, Gareth J. A1 - Shamonin (Chamonine), Mikhail A1 - Geith, Markus A. A1 - Koch, Fabian A1 - Ühlin, Christian A1 - Schratzenstaller, Thomas A1 - Saßmannshausen, Sean Patrick A1 - Auchter, Eberhard A1 - Kriz, Willy A1 - Springer, Othmar A1 - Thumann, Maria A1 - Kusterle, Wolfgang A1 - Obermeier, Andreas A1 - Udalzow, Anton A1 - Schmailzl, Anton A1 - Hierl, Stefan A1 - Langer, Christoph A1 - Schreiner, Rupert ED - Baier, Wolfgang T1 - Forschungsbericht / Ostbayerische Technische Hochschule Regensburg T3 - Forschungsberichte der OTH Regensburg - 2015 Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-13867 SN - 978-3-00-048589-3 CY - Regensburg ER - TY - JOUR A1 - Mauerer, Wolfgang A1 - Rexhepaj, Tanja A1 - Monkman, Gareth J. A1 - Sindersberger, Dirk A1 - Diermeier, Andreas A1 - Neidhart, Thomas A1 - Wolfrum, Dominik A1 - Sterner, Michael A1 - Heberl, Michael A1 - Nusko, Robert A1 - Maier, Georg A1 - Nagl, Klaus A1 - Reuter, Monika A1 - Hofrichter, Andreas A1 - Lex, Thomas A1 - Lesch, Florian A1 - Kieninger, Bärbel A1 - Szalo, Alexander Eduard A1 - Zehner, Alexander A1 - Palm, Christoph A1 - Joblin, Mitchell A1 - Apel, Sven A1 - Ramsauer, Ralf A1 - Lohmann, Daniel A1 - Westner, Markus A1 - Strasser, Artur A1 - Munndi, Maximilian A1 - Ebner, Lena A1 - Elsner, Michael A1 - Weiß, Nils A1 - Segerer, Matthias A1 - Hackenberg, Rudolf A1 - Steger, Sebastian A1 - Schmailzl, Anton A1 - Dostalek, Michael A1 - Armbruster, Dominik A1 - Koch, Fabian A1 - Hierl, Stefan A1 - Thumann, Philipp A1 - Swidergal, Krzysztof A1 - Wagner, Marcus A1 - Briem, Ulrich A1 - Diermeier, Andreas A1 - Spreitzer, Stefan A1 - Beiderbeck, Sabrina A1 - Hook, Christian A1 - Zobel, Martin A1 - Weber, Tim A1 - Groß, Simon A1 - Penzkofer, Rainer A1 - Dendorfer, Sebastian A1 - Schillitz, Ingo A1 - Bauer, Thomas A1 - Rudolph, Clarissa A1 - Schmidt, Katja A1 - Liebetruth, Thomas A1 - Hamer, Markus A1 - Haug, Sonja A1 - Vernim, Matthias A1 - Weber, Karsten A1 - Saßmannshausen, Sean Patrick A1 - Books, Sebastian A1 - Neuleitner, Nikolaus A1 - Rechenauer, Christian A1 - Steffens, Oliver A1 - Kusterle, Wolfgang A1 - Gömmel, Roland A1 - Wellnitz, Felix A1 - Stierstorfer, Johannes A1 - Stadler, Dominik A1 - Hofmann, Matthias J. A1 - Motschmann, Hubert A1 - Shamonin (Chamonine), Mikhail A1 - Bleicher, Veronika A1 - Fischer, Sebastian A1 - Hackenberg, Rudolf A1 - Horn, Anton A1 - Kawasch, Raphael A1 - Petzenhauser, Michael A1 - Probst, Tobias A1 - Udalzow, Anton A1 - Dams, Florian A1 - Schreiner, Rupert A1 - Langer, Christoph A1 - Prommesberger, Christian A1 - Ławrowski, Robert Damian ED - Baier, Wolfgang T1 - Forschungsbericht 2016 T3 - Forschungsberichte der OTH Regensburg - 2016 Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-13840 CY - Regensburg ER - TY - JOUR A1 - Weber, Karsten A1 - Dendorfer, Sebastian A1 - Süß, Franz A1 - Kubowitsch, Simone A1 - Schratzenstaller, Thomas A1 - Haug, Sonja A1 - Mohr, Christa A1 - Kiesl, Hans A1 - Drechsler, Jörg A1 - Westner, Markus A1 - Kobus, Jörn A1 - Schubert, Martin J. W. A1 - Zenger, Stefan A1 - Pietsch, Alexander A1 - Weiß, Josef A1 - Hinterseer, Sebastian A1 - Schieck, Roland A1 - Scherzinger, Stefanie A1 - Klettke, Meike A1 - Ringlstetter, Andreas A1 - Störl, Uta A1 - Bissyandé, Tegawendé F. A1 - Seeburger, Achim A1 - Schindler, Timo A1 - Ramsauer, Ralf A1 - Kiszka, Jan A1 - Kölbl, Andreas A1 - Lohmann, Daniel A1 - Mauerer, Wolfgang A1 - Maier, Johannes A1 - Scorna, Ulrike A1 - Palm, Christoph A1 - Soska, Alexander A1 - Mottok, Jürgen A1 - Ellermeier, Andreas A1 - Vögele, Daniel A1 - Hierl, Stefan A1 - Briem, Ulrich A1 - Buschmann, Knut A1 - Ehrlich, Ingo A1 - Pongratz, Christian A1 - Pielmeier, Benjamin A1 - Tyroller, Quirin A1 - Monkman, Gareth J. A1 - Gut, Franz A1 - Roth, Carina A1 - Hausler, Peter A1 - Bierl, Rudolf A1 - Prommesberger, Christian A1 - Ławrowski, Robert Damian A1 - Langer, Christoph A1 - Schreiner, Rupert A1 - Huang, Yifeng A1 - She, Juncong A1 - Ottl, Andreas A1 - Rieger, Walter A1 - Kraml, Agnes A1 - Poxleitner, Thomas A1 - Hofer, Simon A1 - Heisterkamp, Benjamin A1 - Lerch, Maximilian A1 - Sammer, Nike A1 - Golde, Olivia A1 - Wellnitz, Felix A1 - Schmid, Sandra A1 - Muntschick, Claudia A1 - Kusterle, Wolfgang A1 - Paric, Ivan A1 - Brückl, Oliver A1 - Haslbeck, Matthias A1 - Schmidt, Ottfried A1 - Schwanzer, Peter A1 - Rabl, Hans-Peter A1 - Sterner, Michael A1 - Bauer, Franz A1 - Steinmann, Sven A1 - Eckert, Fabian A1 - Hofrichter, Andreas ED - Baier, Wolfgang T1 - Forschungsbericht 2017 T3 - Forschungsberichte der OTH Regensburg - 2017 KW - Forschung KW - Forschungsbericht Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-13835 SN - 978-3-9818209-3-5 CY - Regensburg ER - TY - JOUR A1 - Lautenschläger, Toni A1 - Leis, Alexander A1 - Dendorfer, Sebastian A1 - Palm, Christoph A1 - Schreiner, Rupert A1 - Langer, Christoph A1 - Prommesberger, Christian A1 - Ławrowski, Robert Damian A1 - Dams, Florian A1 - Bornmann, Benjamin A1 - Navitski, Aliaksandr A1 - Serbun, Pavel A1 - Müller, Günter A1 - Liebetruth, Thomas A1 - Kohlert, Dieter A1 - Pernsteiner, Jochen A1 - Schreier, Franz A1 - Heerklotz, Sabrina A1 - Heerklotz, Allwin A1 - Boos, Alexander A1 - Herwald, Dominik A1 - Monkman, Gareth J. A1 - Treiber, Daniel A1 - Mayer, Matthias A1 - Hörner, Eva A1 - Bentz, Alexander A1 - Shamonin (Chamonine), Mikhail A1 - Johansen, Søren Peter A1 - Reichel, Marco A1 - Stoll, Andrea A1 - Briem, Ulrich A1 - Dullien, Silvia A1 - Renkawitz, Tobias A1 - Weber, Tim A1 - Dendorfer, Sebastian A1 - Grifka, Joachim A1 - Penzkofer, Rainer A1 - Barnsteiner, K. A1 - Jovanovik, M. A1 - Wernecke, P. A1 - Vögele, A. A1 - Bachmann, T. A1 - Plötz, Martin A1 - Schliekmann, Claus A1 - Wels, Harald A1 - Helmberger, Paul A1 - Kaspar, M. A1 - Hönicka, M. A1 - Schrammel, Siegfried A1 - Enser, Markus A1 - Schmidmeier, Monika A1 - Schroll-Decker, Irmgard A1 - Haug, Sonja A1 - Gelfert, Verena A1 - Vernim, Matthias ED - Baier, Wolfgang T1 - Forschungsbericht 2012 T3 - Forschungsberichte der OTH Regensburg - 2012 Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-7834 CY - Regensburg ER - TY - JOUR A1 - Appelhans, Marie-Luise A1 - Kampmann, Matthias A1 - Mottok, Jürgen A1 - Riederer, Michael A1 - Nagl, Klaus A1 - Steffens, Oliver A1 - Dünnweber, Jan A1 - Wildgruber, Markus A1 - Roth, Julius A1 - Stadler, Timo A1 - Palm, Christoph A1 - Weiß, Martin Georg A1 - Rochholz, Sandra A1 - Bierl, Rudolf A1 - Gschossmann, Andreas A1 - Haug, Sonja A1 - Schmidbauer, Simon A1 - Koch, Anna A1 - Westner, Markus A1 - Bary, Benedikt von A1 - Ellermeier, Andreas A1 - Vögele, Daniel A1 - Maiwald, Frederik A1 - Hierl, Stefan A1 - Schlamp, Matthias A1 - Ehrlich, Ingo A1 - Siegl, Marco A1 - Hüntelmann, Sven A1 - Wildfeuer, Matthias A1 - Brückl, Oliver A1 - Sterner, Michael A1 - Hofrichter, Andreas A1 - Eckert, Fabian A1 - Bauer, Franz A1 - Dawoud, Belal A1 - Rabl, Hans-Peter A1 - Gamisch, Bernd A1 - Schmidt, Ottfried A1 - Heberl, Michael A1 - Thema, Martin A1 - Mayer, Ulrike A1 - Eller, Johannes A1 - Sippenauer, Thomas A1 - Adelt, Christian A1 - Haslbeck, Matthias A1 - Vogl, Bettina A1 - Mauerer, Wolfgang A1 - Ramsauer, Ralf A1 - Lohmann, Daniel A1 - Sax, Irmengard A1 - Gabor, Thomas A1 - Feld, Sebastian A1 - Linnhoff-Popien, Claudia A1 - Ławrowski, Robert Damian A1 - Langer, Christoph A1 - Schreiner, Rupert A1 - Sellmair, Josef ED - Baier, Wolfgang T1 - Forschung 2019 BT - Thema: Künstliche Intelligenz N2 - Bericht mit Forschungsprojekten aus verschiedenen Bereichen der OTH Regensburg mit dem Schwerpunktthema "Künstliche Intelligenz" und einem Gespräch zur "Medizin der Zukunft" T3 - Forschungsberichte der OTH Regensburg - 2019 KW - Forschung KW - Forschungsbericht KW - Künstliche Intelligenz Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-7890 SN - 978-3-9818209-7-3 CY - Regensburg ER - TY - JOUR A1 - Römmele, Christoph A1 - Mendel, Robert A1 - Barrett, Caroline A1 - Kiesl, Hans A1 - Rauber, David A1 - Rückert, Tobias A1 - Kraus, Lisa A1 - Heinkele, Jakob A1 - Dhillon, Christine A1 - Grosser, Bianca A1 - Prinz, Friederike A1 - Wanzl, Julia A1 - Fleischmann, Carola A1 - Nagl, Sandra A1 - Schnoy, Elisabeth A1 - Schlottmann, Jakob A1 - Dellon, Evan S. A1 - Messmann, Helmut A1 - Palm, Christoph A1 - Ebigbo, Alanna T1 - An artificial intelligence algorithm is highly accurate for detecting endoscopic features of eosinophilic esophagitis JF - Scientific Reports N2 - The endoscopic features associated with eosinophilic esophagitis (EoE) may be missed during routine endoscopy. We aimed to develop and evaluate an Artificial Intelligence (AI) algorithm for detecting and quantifying the endoscopic features of EoE in white light images, supplemented by the EoE Endoscopic Reference Score (EREFS). An AI algorithm (AI-EoE) was constructed and trained to differentiate between EoE and normal esophagus using endoscopic white light images extracted from the database of the University Hospital Augsburg. In addition to binary classification, a second algorithm was trained with specific auxiliary branches for each EREFS feature (AI-EoE-EREFS). The AI algorithms were evaluated on an external data set from the University of North Carolina, Chapel Hill (UNC), and compared with the performance of human endoscopists with varying levels of experience. The overall sensitivity, specificity, and accuracy of AI-EoE were 0.93 for all measures, while the AUC was 0.986. With additional auxiliary branches for the EREFS categories, the AI algorithm (AI-EoEEREFS) performance improved to 0.96, 0.94, 0.95, and 0.992 for sensitivity, specificity, accuracy, and AUC, respectively. AI-EoE and AI-EoE-EREFS performed significantly better than endoscopy beginners and senior fellows on the same set of images. An AI algorithm can be trained to detect and quantify endoscopic features of EoE with excellent performance scores. The addition of the EREFS criteria improved the performance of the AI algorithm, which performed significantly better than endoscopists with a lower or medium experience level. KW - Artificial Intelligence KW - Smart Endoscopy KW - eosinophilic esophagitis Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-46928 VL - 12 PB - Nature Portfolio CY - London ER - TY - JOUR A1 - Ebigbo, Alanna A1 - Mendel, Robert A1 - Scheppach, Markus W. A1 - Probst, Andreas A1 - Shahidi, Neal A1 - Prinz, Friederike A1 - Fleischmann, Carola A1 - Römmele, Christoph A1 - Gölder, Stefan Karl A1 - Braun, Georg A1 - Rauber, David A1 - Rückert, Tobias A1 - De Souza Jr., Luis Antonio A1 - Papa, João Paulo A1 - Byrne, Michael F. A1 - Palm, Christoph A1 - Messmann, Helmut T1 - Vessel and tissue recognition during third-space endoscopy using a deep learning algorithm JF - Gut N2 - In this study, we aimed to develop an artificial intelligence clinical decision support solution to mitigate operator-dependent limitations during complex endoscopic procedures such as endoscopic submucosal dissection and peroral endoscopic myotomy, for example, bleeding and perforation. A DeepLabv3-based model was trained to delineate vessels, tissue structures and instruments on endoscopic still images from such procedures. The mean cross-validated Intersection over Union and Dice Score were 63% and 76%, respectively. Applied to standardised video clips from third-space endoscopic procedures, the algorithm showed a mean vessel detection rate of 85% with a false-positive rate of 0.75/min. These performance statistics suggest a potential clinical benefit for procedure safety, time and also training. KW - Artificial Intelligence KW - Endoscopy KW - Medical Image Computing Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-54293 VL - 71 IS - 12 SP - 2388 EP - 2390 PB - BMJ CY - London ER - TY - JOUR A1 - Meinikheim, Michael A1 - Mendel, Robert A1 - Palm, Christoph A1 - Probst, Andreas A1 - Muzalyova, Anna A1 - Scheppach, Markus W. A1 - Nagl, Sandra A1 - Schnoy, Elisabeth A1 - Römmele, Christoph A1 - Schulz, Dominik A. H. A1 - Schlottmann, Jakob A1 - Prinz, Friederike A1 - Rauber, David A1 - Rueckert, Tobias A1 - Matsumura, Tomoaki A1 - Fernández-Esparrach, Glòria A1 - Parsa, Nasim A1 - Byrne, Michael F. A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett’s esophagus: a tandem randomized and video trial JF - Endoscopy N2 - Background This study evaluated the effect of an artificial intelligence (AI)-based clinical decision support system on the performance and diagnostic confidence of endoscopists in their assessment of Barrett’s esophagus (BE). Methods 96 standardized endoscopy videos were assessed by 22 endoscopists with varying degrees of BE experience from 12 centers. Assessment was randomized into two video sets: group A (review first without AI and second with AI) and group B (review first with AI and second without AI). Endoscopists were required to evaluate each video for the presence of Barrett’s esophagus-related neoplasia (BERN) and then decide on a spot for a targeted biopsy. After the second assessment, they were allowed to change their clinical decision and confidence level. Results AI had a stand-alone sensitivity, specificity, and accuracy of 92.2%, 68.9%, and 81.3%, respectively. Without AI, BE experts had an overall sensitivity, specificity, and accuracy of 83.3%, 58.1%, and 71.5%, respectively. With AI, BE nonexperts showed a significant improvement in sensitivity and specificity when videos were assessed a second time with AI (sensitivity 69.8% [95%CI 65.2%–74.2%] to 78.0% [95%CI 74.0%–82.0%]; specificity 67.3% [95%CI 62.5%–72.2%] to 72.7% [95%CI 68.2%–77.3%]). In addition, the diagnostic confidence of BE nonexperts improved significantly with AI. Conclusion BE nonexperts benefitted significantly from additional AI. BE experts and nonexperts remained significantly below the stand-alone performance of AI, suggesting that there may be other factors influencing endoscopists’ decisions to follow or discard AI advice. KW - Artificial Intelligence KW - Endoscopy KW - Medical Image Computing Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-72818 PB - Georg Thieme Verlag CY - Stuttgart ER - TY - JOUR A1 - Maier, Andreas A1 - Deserno, Thomas M. A1 - Handels, Heinz A1 - Maier-Hein, Klaus H. A1 - Palm, Christoph A1 - Tolxdorff, Thomas T1 - IJCARS: BVM 2021 special issue JF - International Journal of Computer Assisted Radiology and Surgery N2 - The German workshop on medical image computing (BVM) has been held in different locations in Germany for more than 20 years. In terms of content, BVM focused on the computer-aided analysis of medical image data with a wide range of applications, e.g. in the area of imaging, diagnostics, operation planning, computer-aided intervention and visualization. During this time, there have been remarkable methodological developments and upheavals, on which the BVM community has worked intensively. The area of machine learning should be emphasized, which has led to significant improvements, especially for tasks of classification and segmentation, but increasingly also in image formation and registration. As a result, work in connection with deep learning now dominates the BVM. These developments have also contributed to the establishment of medical image processing at the interface between computer science and medicine as one of the key technologies for the digitization of the health system. In addition to the presentation of current research results, a central aspect of the BVM is primarily the promotion of young scientists from the diverse BVM community, covering not only Germany but also Austria, Switzerland, The Netherland and other European neighbors. The conference serves primarily doctoral students and postdocs, but also students with excellent bachelor and master theses as a platform to present their work, to enter into professional discourse with the community, and to establish networks with specialist colleagues. Despite the many conferences and congresses that are also relevant for medical image processing, the BVM has therefore lost none of its importance and attractiveness and has retained its permanent place in the annual conference rhythm. Building on this foundation, there are some innovations and changes this year. The BVM 2021 was organized for the first time at the Ostbayerische Technische Hochschule Regensburg (OTH Regensburg, a technical university of applied sciences). After Aachen, Berlin, Erlangen, Freiburg, Hamburg, Heidelberg, Leipzig, Lübeck, and Munich, Regensburg is not just a new venue. OTH Regensburg is the first representative of the universities of applied sciences (HAW) to organize the conference, which differs to universities, university hospitals, or research centers like Fraunhofer or Helmholtz. This also considers the further development of the research landscape in Germany, where HAWs increasingly contribute to applied research in addition to their focus on teaching. This development is also reflected in the contributions submitted to the BVM in recent years. At BVM 2021, which was held in a virtual format for the first time due to the Corona pandemic, an attractive and high-quality program was offered. Fortunately, the number of submissions increased significantly. Out of 97 submissions, 26 presentations, 51 posters and 5 software demonstrations were accepted via an anonymized reviewing process with three reviews each. The three best works have been awarded BVM prizes, selected by a separate committee. Based on these high-quality submissions, we are able to present another special issue in the International Journal of Computer Assisted Radiology and Surgery (IJCARS). Out of the 97 submissions, the ones with the highest scores have been invited to submit an extended version of their paper to be presented in IJCARS. As a result, we are now able to present this special issue with seven excellent articles. Many submissions focus on machine learning in a medical context. KW - Medical Image Computing KW - Bildgebendes Verfahren KW - Medizin Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-21666 VL - 16 SP - 2067 EP - 2068 PB - Springer ER - TY - JOUR A1 - Mendel, Robert A1 - Rauber, David A1 - de Souza Jr., Luis Antonio A1 - Papa, João Paulo A1 - Palm, Christoph T1 - Error-Correcting Mean-Teacher: Corrections instead of consistency-targets applied to semi-supervised medical image segmentation JF - Computers in Biology and Medicine N2 - Semantic segmentation is an essential task in medical imaging research. Many powerful deep-learning-based approaches can be employed for this problem, but they are dependent on the availability of an expansive labeled dataset. In this work, we augment such supervised segmentation models to be suitable for learning from unlabeled data. Our semi-supervised approach, termed Error-Correcting Mean-Teacher, uses an exponential moving average model like the original Mean Teacher but introduces our new paradigm of error correction. The original segmentation network is augmented to handle this secondary correction task. Both tasks build upon the core feature extraction layers of the model. For the correction task, features detected in the input image are fused with features detected in the predicted segmentation and further processed with task-specific decoder layers. The combination of image and segmentation features allows the model to correct present mistakes in the given input pair. The correction task is trained jointly on the labeled data. On unlabeled data, the exponential moving average of the original network corrects the student’s prediction. The combined outputs of the students’ prediction with the teachers’ correction form the basis for the semi-supervised update. We evaluate our method with the 2017 and 2018 Robotic Scene Segmentation data, the ISIC 2017 and the BraTS 2020 Challenges, a proprietary Endoscopic Submucosal Dissection dataset, Cityscapes, and Pascal VOC 2012. Additionally, we analyze the impact of the individual components and examine the behavior when the amount of labeled data varies, with experiments performed on two distinct segmentation architectures. Our method shows improvements in terms of the mean Intersection over Union over the supervised baseline and competing methods. Code is available at https://github.com/CloneRob/ECMT. KW - Semi-supervised Segmentation KW - Mean-Teacher KW - Pseudo-labels KW - Medical Imaging Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-57790 SN - 0010-4825 VL - 154 IS - March PB - Elsevier ER - TY - JOUR A1 - Rückert, Tobias A1 - Rückert, Daniel A1 - Palm, Christoph T1 - Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art JF - Computers in Biology and Medicine N2 - In the field of computer- and robot-assisted minimally invasive surgery, enormous progress has been made in recent years based on the recognition of surgical instruments in endoscopic images and videos. In particular, the determination of the position and type of instruments is of great interest. Current work involves both spatial and temporal information, with the idea that predicting the movement of surgical tools over time may improve the quality of the final segmentations. The provision of publicly available datasets has recently encouraged the development of new methods, mainly based on deep learning. In this review, we identify and characterize datasets used for method development and evaluation and quantify their frequency of use in the literature. We further present an overview of the current state of research regarding the segmentation and tracking of minimally invasive surgical instruments in endoscopic images and videos. The paper focuses on methods that work purely visually, without markers of any kind attached to the instruments, considering both single-frame semantic and instance segmentation approaches, as well as those that incorporate temporal information. The publications analyzed were identified through the platforms Google Scholar, Web of Science, and PubMed. The search terms used were “instrument segmentation”, “instrument tracking”, “surgical tool segmentation”, and “surgical tool tracking”, resulting in a total of 741 articles published between 01/2015 and 07/2023, of which 123 were included using systematic selection criteria. A discussion of the reviewed literature is provided, highlighting existing shortcomings and emphasizing the available potential for future developments. KW - Deep Learning KW - Minimal-invasive Chirurgie KW - Bildsegmentierung KW - Surgical instrument segmentation KW - Surgical instrument tracking KW - Spatio-temporal information KW - Endoscopic surgery KW - Robot-assisted surgery Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-69830 N1 - Corresponding author: Tobias Rückert N1 - Corrigendum unter: https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/docId/7033 VL - 169 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Weiherer, Maximilian A1 - Eigenberger, Andreas A1 - Egger, Bernhard A1 - Brébant, Vanessa A1 - Prantl, Lukas A1 - Palm, Christoph T1 - Learning the shape of female breasts: an open-access 3D statistical shape model of the female breast built from 110 breast scans JF - The Visual Computer N2 - We present the Regensburg Breast Shape Model (RBSM)—a 3D statistical shape model of the female breast built from 110 breast scans acquired in a standing position, and the first publicly available. Together with the model, a fully automated, pairwise surface registration pipeline used to establish dense correspondence among 3D breast scans is introduced. Our method is computationally efficient and requires only four landmarks to guide the registration process. A major challenge when modeling female breasts from surface-only 3D breast scans is the non-separability of breast and thorax. In order to weaken the strong coupling between breast and surrounding areas, we propose to minimize the variance outside the breast region as much as possible. To achieve this goal, a novel concept called breast probability masks (BPMs) is introduced. A BPM assigns probabilities to each point of a 3D breast scan, telling how likely it is that a particular point belongs to the breast area. During registration, we use BPMs to align the template to the target as accurately as possible inside the breast region and only roughly outside. This simple yet effective strategy significantly reduces the unwanted variance outside the breast region, leading to better statistical shape models in which breast shapes are quite well decoupled from the thorax. The RBSM is thus able to produce a variety of different breast shapes as independently as possible from the shape of the thorax. Our systematic experimental evaluation reveals a generalization ability of 0.17 mm and a specificity of 2.8 mm. To underline the expressiveness of the proposed model, we finally demonstrate in two showcase applications how the RBSM can be used for surgical outcome simulation and the prediction of a missing breast from the remaining one. Our model is available at https://www.rbsm.re-mic.de/. KW - Statistical shape model KW - Non-rigid surface registration KW - Breast imaging KW - Surgical outcome simulation KW - Breast reconstruction surgery Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-30506 N1 - Corresponding author: Christoph Palm N1 - Zugehörige arXiv-Publikation: https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/docId/2023 VL - 39 IS - 4 SP - 1597 EP - 1616 PB - Springer Nature ER - TY - JOUR A1 - Ebigbo, Alanna A1 - Mendel, Robert A1 - Probst, Andreas A1 - Manzeneder, Johannes A1 - De Souza Jr., Luis Antonio A1 - Papa, João Paulo A1 - Palm, Christoph A1 - Messmann, Helmut T1 - Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma JF - GuT N2 - Computer-aided diagnosis using deep learning (CAD-DL) may be an instrument to improve endoscopic assessment of Barrett’s oesophagus (BE) and early oesophageal adenocarcinoma (EAC). Based on still images from two databases, the diagnosis of EAC by CAD-DL reached sensitivities/specificities of 97%/88% (Augsburg data) and 92%/100% (Medical Image Computing and Computer-Assisted Intervention [MICCAI] data) for white light (WL) images and 94%/80% for narrow band images (NBI) (Augsburg data), respectively. Tumour margins delineated by experts into images were detected satisfactorily with a Dice coefficient (D) of 0.72. This could be a first step towards CAD-DL for BE assessment. If developed further, it could become a useful adjunctive tool for patient management. KW - Speiseröhrenkrebs KW - Diagnose KW - Computerunterstütztes Verfahren KW - Maschinelles Lernen Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-68 N1 - Corresponding authors: Alanna Ebigbo and Christoph Palm VL - 68 IS - 7 SP - 1143 EP - 1145 PB - British Society of Gastroenterology ER - TY - JOUR A1 - De Souza Jr., Luis Antonio A1 - Mendel, Robert A1 - Strasser, Sophia A1 - Ebigbo, Alanna A1 - Probst, Andreas A1 - Messmann, Helmut A1 - Papa, João Paulo A1 - Palm, Christoph T1 - Convolutional Neural Networks for the evaluation of cancer in Barrett’s esophagus: Explainable AI to lighten up the black-box JF - Computers in Biology and Medicine N2 - Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their level of accountability and transparency must be provided in such evaluations. The reliability related to machine learning predictions must be explained and interpreted, especially if diagnosis support is addressed. For this task, the black-box nature of deep learning techniques must be lightened up to transfer its promising results into clinical practice. Hence, we aim to investigate the use of explainable artificial intelligence techniques to quantitatively highlight discriminative regions during the classification of earlycancerous tissues in Barrett’s esophagus-diagnosed patients. Four Convolutional Neural Network models (AlexNet, SqueezeNet, ResNet50, and VGG16) were analyzed using five different interpretation techniques (saliency, guided backpropagation, integrated gradients, input × gradients, and DeepLIFT) to compare their agreement with experts’ previous annotations of cancerous tissue. We could show that saliency attributes match best with the manual experts’ delineations. Moreover, there is moderate to high correlation between the sensitivity of a model and the human-and-computer agreement. The results also lightened that the higher the model’s sensitivity, the stronger the correlation of human and computational segmentation agreement. We observed a relevant relation between computational learning and experts’ insights, demonstrating how human knowledge may influence the correct computational learning. KW - Deep Learning KW - Künstliche Intelligenz KW - Computerunterstützte Medizin KW - Barrett's esophagus KW - Adenocarcinoma KW - Machine learning KW - Explainable artificial intelligence KW - Computer-aided diagnosis Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-20126 SN - 0010-4825 VL - 135 SP - 1 EP - 14 PB - Elsevier ER - TY - JOUR A1 - Hartmann, Robin A1 - Nieberle, Felix A1 - Palm, Christoph A1 - Brébant, Vanessa A1 - Prantl, Lukas A1 - Kuehle, Reinald A1 - Reichert, Torsten E. A1 - Taxis, Juergen A1 - Ettl, Tobias T1 - Utility of Smartphone-based Three-dimensional Surface Imaging for Digital Facial Anthropometry JF - JPRAS Open N2 - Background The utilization of three-dimensional (3D) surface imaging for facial anthropometry is a significant asset for patients undergoing maxillofacial surgery. Notably, there have been recent advancements in smartphone technology that enable 3D surface imaging. In this study, anthropometric assessments of the face were performed using a smartphone and a sophisticated 3D surface imaging system. Methods 30 healthy volunteers (15 females and 15 males) were included in the study. An iPhone 14 Pro (Apple Inc., USA) using the application 3D Scanner App (Laan Consulting Corp., USA) and the Vectra M5 (Canfield Scientific, USA) were employed to create 3D surface models. For each participant, 19 anthropometric measurements were conducted on the 3D surface models. Subsequently, the anthropometric measurements generated by the two approaches were compared. The statistical techniques employed included the paired t-test, paired Wilcoxon signed-rank test, Bland–Altman analysis, and calculation of the intraclass correlation coefficient (ICC). Results All measurements showed excellent agreement between smartphone-based and Vectra M5-based measurements (ICC between 0.85 and 0.97). Statistical analysis revealed no statistically significant differences in the central tendencies for 17 of the 19 linear measurements. Despite the excellent agreement found, Bland–Altman analysis revealed that the 95% limits of agreement between the two methods exceeded ±3 mm for the majority of measurements. Conclusion Digital facial anthropometry using smartphones can serve as a valuable supplementary tool for surgeons, enhancing their communication with patients. However, the proposed data suggest that digital facial anthropometry using smartphones may not yet be suitable for certain diagnostic purposes that require high accuracy. KW - Three-dimensional surface imaging KW - Stereophotogrammetry KW - Smartphone-based surface imaging KW - Digital anthropometry KW - Facial anthropometry Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-70348 VL - 39 SP - 330 EP - 343 PB - Elsevier ER - TY - JOUR A1 - Tobias, Rueckert A1 - Daniel, Rueckert A1 - Palm, Christoph T1 - Corrigendum to “Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art” [Comput. Biol. Med. 169 (2024) 107929] JF - Computers in Biology and Medicine N2 - The authors regret that the SAR-RARP50 dataset is missing from the description of publicly available datasets presented in Chapter 4. Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-70337 N1 - Aufsatz unter: https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/docId/6983 PB - Elsevier ER - TY - INPR A1 - Allan, Max A1 - Kondo, Satoshi A1 - Bodenstedt, Sebastian A1 - Leger, Stefan A1 - Kadkhodamohammadi, Rahim A1 - Luengo, Imanol A1 - Fuentes, Felix A1 - Flouty, Evangello A1 - Mohammed, Ahmed A1 - Pedersen, Marius A1 - Kori, Avinash A1 - Alex, Varghese A1 - Krishnamurthi, Ganapathy A1 - Rauber, David A1 - Mendel, Robert A1 - Palm, Christoph A1 - Bano, Sophia A1 - Saibro, Guinther A1 - Shih, Chi-Sheng A1 - Chiang, Hsun-An A1 - Zhuang, Juntang A1 - Yang, Junlin A1 - Iglovikov, Vladimir A1 - Dobrenkii, Anton A1 - Reddiboina, Madhu A1 - Reddy, Anubhav A1 - Liu, Xingtong A1 - Gao, Cong A1 - Unberath, Mathias A1 - Kim, Myeonghyeon A1 - Kim, Chanho A1 - Kim, Chaewon A1 - Kim, Hyejin A1 - Lee, Gyeongmin A1 - Ullah, Ihsan A1 - Luna, Miguel A1 - Park, Sang Hyun A1 - Azizian, Mahdi A1 - Stoyanov, Danail A1 - Maier-Hein, Lena A1 - Speidel, Stefanie T1 - 2018 Robotic Scene Segmentation Challenge N2 - In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of exvivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models. However, the limited background variation and simple motion rendered the dataset uninformative in learning about which techniques would be suitable for segmentation in real surgery. In 2017, at the same workshop in Quebec we introduced the robotic instrument segmentation dataset with 10 teams participating in the challenge to perform binary, articulating parts and type segmentation of da Vinci instruments. This challenge included realistic instrument motion and more complex porcine tissue as background and was widely addressed with modfications on U-Nets and other popular CNN architectures [1]. In 2018 we added to the complexity by introducing a set of anatomical objects and medical devices to the segmented classes. To avoid over-complicating the challenge, we continued with porcine data which is dramatically simpler than human tissue due to the lack of fatty tissue occluding many organs. KW - Minimally invasive surgery KW - Robotic KW - Minimal-invasive Chirurgie KW - Robotik Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-50049 UR - https://arxiv.org/abs/2001.11190 ER -