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 2015 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 - 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, Marcel 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 - 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 - Beyer, Thomas A1 - Weigert, Markus A1 - Quick, Harald H. A1 - Pietrzyk, Uwe A1 - Vogt, Florian A1 - Palm, Christoph A1 - Antoch, Gerald A1 - Müller, Stefan P. A1 - Bockisch, Andreas T1 - MR-based attenuation correction for torso-PET/MR imaging BT - pitfalls in mapping MR to CT data JF - European Journal of Nuclear Medicine and Molecular Imaging N2 - Purpose MR-based attenuation correction (AC) will become an integral part of combined PET/MR systems. Here, we propose a toolbox to validate MR-AC of clinical PET/MRI data sets. Methods Torso scans of ten patients were acquired on a combined PET/CT and on a 1.5-T MRI system. MR-based attenuation data were derived from the CT following MR–CT image co-registration and subsequent histogram matching. PET images were reconstructed after CT- (PET/CT) and MR-based AC (PET/MRI). Lesion-to-background (L/B) ratios were estimated on PET/CT and PET/MRI. Results MR–CT histogram matching leads to a mean voxel intensity difference in the CT- and MR-based attenuation images of 12% (max). Mean differences between PET/MRI and PET/CT were 19% (max). L/B ratios were similar except for the lung where local misregistration and intensity transformation leads to a biased PET/MRI. Conclusion Our toolbox can be used to study pitfalls in MR-AC. We found that co-registration accuracy and pixel value transformation determine the accuracy of PET/MRI. KW - PET/MRI KW - PET/CT KW - Attenuation correction KW - Kernspintomografie KW - Positronen-Emissions-Tomografie KW - Schwächung Y1 - 2008 U6 - https://doi.org/10.1007/s00259-008-0734-0 VL - 35 IS - 6 SP - 1142 EP - 1146 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 - 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 - Souza Jr., Luis Antonio de 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 - Knödler, Leonard A1 - Baecher, Helena A1 - Kauke-Navarro, Martin A1 - Prantl, Lukas A1 - Machens, Hans-Günther A1 - Scheuermann, Philipp A1 - Palm, Christoph A1 - Baumann, Raphael A1 - Kehrer, Andreas A1 - Panayi, Adriana C. A1 - Knoedler, Samuel T1 - Towards a Reliable and Rapid Automated Grading System in Facial Palsy Patients: Facial Palsy Surgery Meets Computer Science JF - Journal of Clinical Medicine N2 - Background: Reliable, time- and cost-effective, and clinician-friendly diagnostic tools are cornerstones in facial palsy (FP) patient management. Different automated FP grading systems have been developed but revealed persisting downsides such as insufficient accuracy and cost-intensive hardware. We aimed to overcome these barriers and programmed an automated grading system for FP patients utilizing the House and Brackmann scale (HBS). Methods: Image datasets of 86 patients seen at the Department of Plastic, Hand, and Reconstructive Surgery at the University Hospital Regensburg, Germany, between June 2017 and May 2021, were used to train the neural network and evaluate its accuracy. Nine facial poses per patient were analyzed by the algorithm. Results: The algorithm showed an accuracy of 100%. Oversampling did not result in altered outcomes, while the direct form displayed superior accuracy levels when compared to the modular classification form (n = 86; 100% vs. 99%). The Early Fusion technique was linked to improved accuracy outcomes in comparison to the Late Fusion and sequential method (n = 86; 100% vs. 96% vs. 97%). Conclusions: Our automated FP grading system combines high-level accuracy with cost- and time-effectiveness. Our algorithm may accelerate the grading process in FP patients and facilitate the FP surgeon’s workflow. Y1 - 2022 U6 - https://doi.org/10.3390/jcm11174998 VL - 11 IS - 17 PB - MDPI CY - Basel ER - TY - JOUR A1 - Souza Jr., Luis Antonio de A1 - Palm, Christoph A1 - Mendel, Robert A1 - Hook, Christian A1 - Ebigbo, Alanna A1 - Probst, Andreas A1 - Messmann, Helmut A1 - Weber, Silke A. T. A1 - Papa, João Paulo T1 - A survey on Barrett's esophagus analysis using machine learning JF - Computers in Biology and Medicine N2 - This work presents a systematic review concerning recent studies and technologies of machine learning for Barrett's esophagus (BE) diagnosis and treatment. The use of artificial intelligence is a brand new and promising way to evaluate such disease. We compile some works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer, and Hindawi Publishing Corporation. Each selected work has been analyzed to present its objective, methodology, and results. The BE progression to dysplasia or adenocarcinoma shows a complex pattern to be detected during endoscopic surveillance. Therefore, it is valuable to assist its diagnosis and automatic identification using computer analysis. The evaluation of the BE dysplasia can be performed through manual or automated segmentation through machine learning techniques. Finally, in this survey, we reviewed recent studies focused on the automatic detection of the neoplastic region for classification purposes using machine learning methods. KW - Speiseröhrenkrankheit KW - Diagnose KW - Mustererkennung KW - Maschinelles Lernen KW - Literaturbericht KW - Barrett's esophagus KW - Machine learning KW - Adenocarcinoma KW - Image processing KW - Pattern recognition KW - Computer-aided diagnosis Y1 - 2018 U6 - https://doi.org/10.1016/j.compbiomed.2018.03.014 VL - 96 SP - 203 EP - 213 PB - Elsevier ER -