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 - BOOK A1 - Reichenbach, Hartmut A1 - Bruss, Werner A1 - Bürger, Michael A1 - Fiedler, Manfred A1 - Göbel, Manuel A1 - Middendorf, Bernhard A1 - Neidhart, Thomas A1 - Pollnow, Elke A1 - Ramler-Kowollik, Marcel A1 - Scholz, Enrico A1 - Simon, Jana A1 - Sosinka, Katharina A1 - Spirkl, Florian A1 - Trunk, Ulrich A1 - Wagner, Achim A1 - Wagner, Bernd A1 - Weber, Steffen ED - Forschungsgesellschaft für Straßen- und Verkehrswesen FGSV e.V., T1 - Merkblatt für die Herstellung und Verwendung von zeitweise fließfähigen, selbstverdichtenden Verfüllbaustoffen (ZFSV) im Erdbau N2 - Das „Merkblatt für die Herstellung und Verwendung von zeitweise fließfähigen, selbstverdichtenden Verfüllbaustoffen“ (M ZFSV), Ausgabe 2025, befasst sich mit der Herstellung und der Anwendung zeitweise fließfähiger, selbstverdichtender Verfüllbaustoffe (ZFSV) gemäß den „Zusätzlichen Technischen Vertragsbedingungen und Richtlinien für Erdarbeiten im Straßenbau“ (ZTV E-StB) (FGSV 599) beim Bau von Straßen und Verkehrsflächen. Für andere Bereiche des Bauwesens kann dieses Merkblatt ebenfalls angewendet werden. Das M ZFSV enthält Qualitätsanforderungen für diese Baustoffe und Baustoffgemische. Die Anforderungen schließen die verschiedenen Anwendungsgebiete der Baustoffe und Baustoffgemische, die Herstellung, Lieferung und den Einbau sowie die Prüfungen und die Qualitätssicherung ein. Vorab wird ebenso auf Anwendungsgebiete und Planungsgrundsätze eingegangen. Ergänzt werden die umfangreichen Ausführungen durch mehrere Anhänge. Y1 - 2025 SN - 978-3-86446-419-5 PB - FGSV Verlag CY - Köln ET - Ausgabe 2025 ER - TY - GEN A1 - Scheppach, Markus W. A1 - Nunes, Danilo Weber A1 - Arizi, X. A1 - Rauber, David A1 - Probst, Andreas A1 - Nagl, Sandra A1 - Römmele, Christoph A1 - Meinikheim, Michael A1 - Palm, Christoph A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Procedural phase recognition in endoscopic submucosal dissection (ESD) using artificial intelligence (AI) T2 - Endoscopy N2 - Aims Recent evidence suggests the possibility of intraprocedural phase recognition in surgical operations as well as endoscopic interventions such as peroral endoscopic myotomy and endoscopic submucosal dissection (ESD) by AI-algorithms. The intricate measurement of intraprocedural phase distribution may deepen the understanding of the procedure. Furthermore, real-time quality assessment as well as automation of reporting may become possible. Therefore, we aimed to develop an AI-algorithm for intraprocedural phase recognition during ESD. Methods A training dataset of 364385 single images from 9 full-length ESD videos was compiled. Each frame was classified into one procedural phase. Phases included scope manipulation, marking, injection, application of electrical current and bleeding. Allocation of each frame was only possible to one category. This training dataset was used to train a Video Swin transformer to recognize the phases. Temporal information was included via logarithmic frame sampling. Validation was performed using two separate ESD videos with 29801 single frames. Results The validation yielded sensitivities of 97.81%, 97.83%, 95.53%, 85.01% and 87.55% for scope manipulation, marking, injection, electric application and bleeding, respectively. Specificities of 77.78%, 90.91%, 95.91%, 93.65% and 84.76% were measured for the same parameters. Conclusions The developed algorithm was able to classify full-length ESD videos on a frame-by-frame basis into the predefined classes with high sensitivities and specificities. Future research will aim at the development of quality metrics based on single-operator phase distribution. Y1 - 2024 U6 - https://doi.org/10.1055/s-0044-1783804 VL - 56 IS - S 02 SP - S439 PB - Thieme CY - Stuttgart ER - TY - CHAP A1 - Nunes, Danilo Weber A1 - Hammer, Michael A1 - Hammer, Simone A1 - Uller, Wibke A1 - Palm, Christoph T1 - Classification of Vascular Malformations Based on T2 STIR Magnetic Resonance Imaging T2 - Bildverarbeitung für die Medizin 2022: Proceedings, German Workshop on Medical Image Computing, Heidelberg, June 26-28, 2022 N2 - Vascular malformations (VMs) are a rare condition. They can be categorized into high-flow and low-flow VMs, which is a challenging task for radiologists. In this work, a very heterogeneous set of MRI images with only rough annotations are used for classification with a convolutional neural network. The main focus is to describe the challenging data set and strategies to deal with such data in terms of preprocessing, annotation usage and choice of the network architecture. We achieved a classification result of 89.47 % F1-score with a 3D ResNet 18. KW - Deep Learning KW - Magnetic Resonance Imaging KW - Vascular Malformations Y1 - 2022 U6 - https://doi.org/10.1007/978-3-658-36932-3_57 SP - 267 EP - 272 PB - Springer Vieweg CY - Wiesbaden ER -