{"paper":{"title":"Robust Medical Instrument Segmentation Challenge 2019","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Annette Kopp-Schneider, Annika Reinke, Beat P. M\\\"uller-Stich, Cristina Gonz\\'alez, Debesh Jha, Diana Mindroc Filimon, Dong Guo, Enes Hosgor, Fabian Isensee, Gui-Bin Bian, Gutai Wang, Hannes Kenngott, Hellena Hempe, Hua-Bin Chen, Isabell Twick, Jiacheng Wang, Jon Lindstr\\\"om Bolmgren, Kadir Kirtac, Klaus H. Maier-Hein, Klaus Schoeffmann, Laura Bravo-S\\'anchez, Lei Zhu, Lena Maier-Hein, Liansheng Wang, Lu Wang, Manuel Wiesenfarth, Martin Apitz, Martin Wagner, Michael A. Riegler, Michael Stenzel, P{\\aa}l Halvorsen, Pablo Arbel\\'aez, Patrick Scholz, Peter M. Full, Pheng-Ann Heng, Pierangela Bruno, Ruohua Shi, Sabrina Kletz, Sebastian Bodenstedt, Stefanie Speidel, Stefan Leger, Thuy Nuong Tran, Tingting Jiang, Tobias Ross, Yan-Jie Zhou, Yueming Jin, Yujie Zhang, Zeng-Guang Hou, Zhen-Liang Ni, Zhixuan Li","submitted_at":"2020-03-23T14:35:08Z","abstract_excerpt":"Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization; algorithms trained for a specific intervention in a specific hospital should ge"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.10299","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2003.10299/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}