@misc{ScharfenbergMottokArtmannetal., author = {Scharfenberg, Georg and Mottok, J{\"u}rgen and Artmann, Christina and Hobelsberger, Martin and Paric, Ivan and Großmann, Benjamin and Pohlt, Clemens and Wackerbarth, Alena and Pausch, Uli and Heidrich, Christiane and Fadanelli, Martin and Elsner, Michael and P{\"o}cher, Daniel and Pittroff, Lenz and Beer, Stefan and Br{\"u}ckl, Oliver and Haslbeck, Matthias and Sterner, Michael and Thema, Martin and Muggenthaler, Nicole and Lenck, Thorsten and G{\"o}tz, Philipp and Eckert, Fabian and Deubzer, Michael and Stingl, Armin and Simsek, Erol and Kr{\"a}mer, Stefan and Großmann, Benjamin and Schlegl, Thomas and Niedersteiner, Sascha and Berlehner, Thomas and Joblin, Mitchell and Mauerer, Wolfgang and Apel, Sven and Siegmund, Janet and Riehle, Dirk and Weber, Joachim and Palm, Christoph and Zobel, Martin and Al-Falouji, Ghassan and Prestel, Dietmar and Scharfenberg, Georg and Mandl, Roland and Deinzer, Arnulf and Halang, W. and Margraf-Stiksrud, Jutta and Sick, Bernhard and Deinzer, Renate and Scherzinger, Stefanie and Klettke, Meike and St{\"o}rl, Uta and Wiech, Katharina and Kubata, Christoph and Sindersberger, Dirk and Monkman, Gareth J. and Dollinger, Markus and Dembianny, Sven and K{\"o}lbl, Andreas and Welker, Franz and Meier, Matthias and Thumann, Philipp and Swidergal, Krzysztof and Wagner, Marcus and Haug, Sonja and Vernim, Matthias and Seidenst{\"u}cker, Barbara and Weber, Karsten and Arsan, Christian and Schone, Reinhold and M{\"u}nder, Johannes and Schroll-Decker, Irmgard and Dillinger, Andrea Elisabeth and Fuchshofer, Rudolf and Monkman, Gareth J. and Shamonin (Chamonine), Mikhail and Geith, Markus A. and Koch, Fabian and {\"U}hlin, Christian and Schratzenstaller, Thomas and Saßmannshausen, Sean Patrick and Auchter, Eberhard and Kriz, Willy and Springer, Othmar and Thumann, Maria and Kusterle, Wolfgang and Obermeier, Andreas and Udalzow, Anton and Schmailzl, Anton and Hierl, Stefan and Langer, Christoph and Schreiner, Rupert}, title = {Forschungsbericht 2015}, editor = {Baier, Wolfgang}, address = {Regensburg}, organization = {Ostbayerische Technische Hochschule Regensburg}, isbn = {978-3-00-048589-3}, doi = {10.35096/othr/pub-1386}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-13867}, language = {de} } @misc{MauererRexhepajMonkmanetal., author = {Mauerer, Wolfgang and Rexhepaj, Tanja and Monkman, Gareth J. and Sindersberger, Dirk and Diermeier, Andreas and Neidhart, Thomas and Wolfrum, Dominik and Sterner, Michael and Heberl, Michael and Nusko, Robert and Maier, Georg and Nagl, Klaus and Reuter, Monika and Hofrichter, Andreas and Lex, Thomas and Lesch, Florian and Kieninger, B{\"a}rbel and Szalo, Alexander Eduard and Zehner, Alexander and Palm, Christoph and Joblin, Mitchell and Apel, Sven and Ramsauer, Ralf and Lohmann, Daniel and Westner, Markus and Strasser, Artur and Munndi, Maximilian and Ebner, Lena and Elsner, Michael and Weiß, Nils and Segerer, Matthias and Hackenberg, Rudolf and Steger, Sebastian and Schmailzl, Anton and Dostalek, Michael and Armbruster, Dominik and Koch, Fabian and Hierl, Stefan and Thumann, Philipp and Swidergal, Krzysztof and Wagner, Marcus and Briem, Ulrich and Diermeier, Andreas and Spreitzer, Stefan and Beiderbeck, Sabrina and Hook, Christian and Zobel, Martin and Weber, Tim and Groß, Simon and Penzkofer, Rainer and Dendorfer, Sebastian and Schillitz, Ingo and Bauer, Thomas and Rudolph, Clarissa and Schmidt, Katja and Liebetruth, Thomas and Hamer, Markus and Haug, Sonja and Vernim, Matthias and Weber, Karsten and Saßmannshausen, Sean Patrick and Books, Sebastian and Neuleitner, Nikolaus and Rechenauer, Christian and Steffens, Oliver and Kusterle, Wolfgang and G{\"o}mmel, Roland and Wellnitz, Felix and Stierstorfer, Johannes and Stadler, Dominik and Hofmann, Matthias J. and Motschmann, Hubert and Shamonin (Chamonine), Mikhail and Bleicher, Veronika and Fischer, Sebastian and Hackenberg, Rudolf and Horn, Anton and Kawasch, Raphael and Petzenhauser, Michael and Probst, Tobias and Udalzow, Anton and Dams, Florian and Schreiner, Rupert and Langer, Christoph and Prommesberger, Christian and Ławrowski, Robert Damian}, title = {Forschungsbericht 2016}, editor = {Baier, Wolfgang}, address = {Regensburg}, organization = {Ostbayerische Technische Hochschule Regensburg}, doi = {10.35096/othr/pub-1384}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-13840}, language = {de} } @misc{LautenschlaegerLeisDendorferetal., author = {Lautenschl{\"a}ger, Toni and Leis, Alexander and Dendorfer, Sebastian and Palm, Christoph and Schreiner, Rupert and Langer, Christoph and Prommesberger, Christian and Ławrowski, Robert Damian and Dams, Florian and Bornmann, Benjamin and Navitski, Aliaksandr and Serbun, Pavel and M{\"u}ller, G{\"u}nter and Liebetruth, Thomas and Kohlert, Dieter and Pernsteiner, Jochen and Schreier, Franz and Heerklotz, Sabrina and Heerklotz, Allwin and Boos, Alexander and Herwald, Dominik and Monkman, Gareth J. and Treiber, Daniel and Mayer, Matthias and H{\"o}rner, Eva and Bentz, Alexander and Shamonin (Chamonine), Mikhail and Johansen, S{\o}ren Peter and Reichel, Marco and Stoll, Andrea and Briem, Ulrich and Dullien, Silvia and Renkawitz, Tobias and Weber, Tim and Dendorfer, Sebastian and Grifka, Joachim and Penzkofer, Rainer and Barnsteiner, K. and Jovanovik, M. and Wernecke, P. and V{\"o}gele, A. and Bachmann, T. and Pl{\"o}tz, Martin and Schliekmann, Claus and Wels, Harald and Helmberger, Paul and Kaspar, Marcel and H{\"o}nicka, M. and Schrammel, Siegfried and Enser, Markus and Schmidmeier, Monika and Schroll-Decker, Irmgard and Haug, Sonja and Gelfert, Verena and Vernim, Matthias}, title = {Forschungsbericht 2012}, editor = {Baier, Wolfgang}, address = {Regensburg}, organization = {Ostbayerische Technische Hochschule Regensburg}, doi = {10.35096/othr/pub-783}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-7834}, pages = {64}, language = {de} } @misc{AppelhansKampmannMottoketal., author = {Appelhans, Marie-Luise and Kampmann, Matthias and Mottok, J{\"u}rgen and Riederer, Michael and Nagl, Klaus and Steffens, Oliver and D{\"u}nnweber, Jan and Wildgruber, Markus and Roth, Julius and Stadler, Timo and Palm, Christoph and Weiß, Martin Georg and Rochholz, Sandra and Bierl, Rudolf and Gschossmann, Andreas and Haug, Sonja and Schmidbauer, Simon and Koch, Anna and Westner, Markus and Bary, Benedikt von and Ellermeier, Andreas and V{\"o}gele, Daniel and Maiwald, Frederik and Hierl, Stefan and Schlamp, Matthias and Ehrlich, Ingo and Siegl, Marco and H{\"u}ntelmann, Sven and Wildfeuer, Matthias and Br{\"u}ckl, Oliver and Sterner, Michael and Hofrichter, Andreas and Eckert, Fabian and Bauer, Franz and Dawoud, Belal and Rabl, Hans-Peter and Gamisch, Bernd and Schmidt, Ottfried and Heberl, Michael and Thema, Martin and Mayer, Ulrike and Eller, Johannes and Sippenauer, Thomas and Adelt, Christian and Haslbeck, Matthias and Vogl, Bettina and Mauerer, Wolfgang and Ramsauer, Ralf and Lohmann, Daniel and Sax, Irmengard and Gabor, Thomas and Feld, Sebastian and Linnhoff-Popien, Claudia and Ławrowski, Robert Damian and Langer, Christoph and Schreiner, Rupert and Sellmair, Josef}, title = {Forschung 2019}, editor = {Baier, Wolfgang}, address = {Regensburg}, organization = {Ostbayerische Technische Hochschule Regensburg}, isbn = {978-3-9818209-7-3}, doi = {10.35096/othr/pub-789}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-7890}, pages = {72}, abstract = {Bericht mit Forschungsprojekten aus verschiedenen Bereichen der OTH Regensburg mit dem Schwerpunktthema "K{\"u}nstliche Intelligenz" und einem Gespr{\"a}ch zur "Medizin der Zukunft"}, subject = {Forschung}, language = {de} } @misc{WeberDendorferSuessetal., author = {Weber, Karsten and Dendorfer, Sebastian and S{\"u}ß, Franz and Kubowitsch, Simone and Schratzenstaller, Thomas and Haug, Sonja and Mohr, Christa and Kiesl, Hans and Drechsler, J{\"o}rg and Westner, Markus and Kobus, J{\"o}rn and Schubert, Martin J. W. and Zenger, Stefan and Pietsch, Alexander and Weiß, Josef and Hinterseer, Sebastian and Schieck, Roland and Scherzinger, Stefanie and Klettke, Meike and Ringlstetter, Andreas and St{\"o}rl, Uta and Bissyand{\´e}, Tegawend{\´e} F. and Seeburger, Achim and Schindler, Timo and Ramsauer, Ralf and Kiszka, Jan and K{\"o}lbl, Andreas and Lohmann, Daniel and Mauerer, Wolfgang and Maier, Johannes and Scorna, Ulrike and Palm, Christoph and Soska, Alexander and Mottok, J{\"u}rgen and Ellermeier, Andreas and V{\"o}gele, Daniel and Hierl, Stefan and Briem, Ulrich and Buschmann, Knut and Ehrlich, Ingo and Pongratz, Christian and Pielmeier, Benjamin and Tyroller, Quirin and Monkman, Gareth J. and Gut, Franz and Roth, Carina and Hausler, Peter and Bierl, Rudolf and Prommesberger, Christian and Ławrowski, Robert Damian and Langer, Christoph and Schreiner, Rupert and Huang, Yifeng and She, Juncong and Ottl, Andreas and Rieger, Walter and Kraml, Agnes and Poxleitner, Thomas and Hofer, Simon and Heisterkamp, Benjamin and Lerch, Maximilian and Sammer, Nike and Golde, Olivia and Wellnitz, Felix and Schmid, Sandra and Muntschick, Claudia and Kusterle, Wolfgang and Paric, Ivan and Br{\"u}ckl, Oliver and Haslbeck, Matthias and Schmidt, Ottfried and Schwanzer, Peter and Rabl, Hans-Peter and Sterner, Michael and Bauer, Franz and Steinmann, Sven and Eckert, Fabian and Hofrichter, Andreas}, title = {Forschungsbericht 2017}, editor = {Baier, Wolfgang}, address = {Regensburg}, organization = {Ostbayerische Technische Hochschule Regensburg}, isbn = {978-3-9818209-3-5}, doi = {10.35096/othr/pub-1383}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-13835}, subject = {Forschung}, language = {de} } @article{BeyerWeigertQuicketal., author = {Beyer, Thomas and Weigert, Markus and Quick, Harald H. and Pietrzyk, Uwe and Vogt, Florian and Palm, Christoph and Antoch, Gerald and M{\"u}ller, Stefan P. and Bockisch, Andreas}, title = {MR-based attenuation correction for torso-PET/MR imaging}, series = {European Journal of Nuclear Medicine and Molecular Imaging}, volume = {35}, journal = {European Journal of Nuclear Medicine and Molecular Imaging}, number = {6}, doi = {10.1007/s00259-008-0734-0}, pages = {1142 -- 1146}, abstract = {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.}, subject = {Kernspintomografie}, language = {en} } @article{HartmannNieberlePalmetal., author = {Hartmann, Robin and Nieberle, Felix and Palm, Christoph and Br{\´e}bant, Vanessa and Prantl, Lukas and Kuehle, Reinald and Reichert, Torsten E. and Taxis, Juergen and Ettl, Tobias}, title = {Utility of Smartphone-based Three-dimensional Surface Imaging for Digital Facial Anthropometry}, series = {JPRAS Open}, volume = {39}, journal = {JPRAS Open}, publisher = {Elsevier}, doi = {10.1016/j.jpra.2024.01.014}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-70348}, pages = {330 -- 343}, abstract = {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.}, language = {en} } @article{EbigboMendelScheppachetal., author = {Ebigbo, Alanna and Mendel, Robert and Scheppach, Markus W. and Probst, Andreas and Shahidi, Neal and Prinz, Friederike and Fleischmann, Carola and R{\"o}mmele, Christoph and G{\"o}lder, Stefan Karl and Braun, Georg and Rauber, David and R{\"u}ckert, Tobias and Souza Jr., Luis Antonio de and Papa, Jo{\~a}o Paulo and Byrne, Michael F. and Palm, Christoph and Messmann, Helmut}, title = {Vessel and tissue recognition during third-space endoscopy using a deep learning algorithm}, series = {Gut}, volume = {71}, journal = {Gut}, number = {12}, publisher = {BMJ}, address = {London}, doi = {10.1136/gutjnl-2021-326470}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-54293}, pages = {2388 -- 2390}, abstract = {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.}, language = {en} } @article{KnoedlerBaecherKaukeNavarroetal., author = {Kn{\"o}dler, Leonard and Baecher, Helena and Kauke-Navarro, Martin and Prantl, Lukas and Machens, Hans-G{\"u}nther and Scheuermann, Philipp and Palm, Christoph and Baumann, Raphael and Kehrer, Andreas and Panayi, Adriana C. and Knoedler, Samuel}, title = {Towards a Reliable and Rapid Automated Grading System in Facial Palsy Patients: Facial Palsy Surgery Meets Computer Science}, series = {Journal of Clinical Medicine}, volume = {11}, journal = {Journal of Clinical Medicine}, number = {17}, publisher = {MDPI}, address = {Basel}, doi = {10.3390/jcm11174998}, abstract = {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.}, language = {en} } @article{SouzaJrPalmMendeletal., author = {Souza Jr., Luis Antonio de and Palm, Christoph and Mendel, Robert and Hook, Christian and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Weber, Silke A. T. and Papa, Jo{\~a}o Paulo}, title = {A survey on Barrett's esophagus analysis using machine learning}, series = {Computers in Biology and Medicine}, volume = {96}, journal = {Computers in Biology and Medicine}, publisher = {Elsevier}, doi = {10.1016/j.compbiomed.2018.03.014}, pages = {203 -- 213}, abstract = {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.}, subject = {Speiser{\"o}hrenkrankheit}, language = {en} }