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| Verfasst von: | Nwoye, Chinedu [VerfasserIn]  |
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| | Yu, Tong [VerfasserIn] |
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| | Sharma, Saurav [VerfasserIn] |
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| | Murali, Aditya [VerfasserIn] |
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| | Alapatt, Deepak [VerfasserIn] |
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| | Vardazaryan, Armine [VerfasserIn] |
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| | Yuan, Kun [VerfasserIn] |
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| | Hajek, Jonas [VerfasserIn] |
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| | Reiter, Wolfgang [VerfasserIn] |
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| | Yamlahi, Amine [VerfasserIn] |
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| | Smidt, Finn-Henri [VerfasserIn] |
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| | Zou, Xiaoyang [VerfasserIn] |
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| | Zheng, Guoyan [VerfasserIn] |
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| | Oliveira, Bruno [VerfasserIn] |
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| | Torres, Helena R. [VerfasserIn] |
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| | Kondo, Satoshi [VerfasserIn] |
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| | Kasai, Satoshi [VerfasserIn] |
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| | Holm, Felix [VerfasserIn] |
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| | Özsoy, Ege [VerfasserIn] |
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| | Gui, Shuangchun [VerfasserIn] |
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| | Li, Han [VerfasserIn] |
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| | Raviteja, Sista [VerfasserIn] |
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| | Sathish, Rachana [VerfasserIn] |
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| | Poudel, Pranav [VerfasserIn] |
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| | Bhattarai, Binod [VerfasserIn] |
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| | Wang, Ziheng [VerfasserIn] |
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| | Rui, Guo [VerfasserIn] |
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| | Schellenberg, Melanie [VerfasserIn] |
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| | Vilaça, João L. [VerfasserIn] |
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| | Czempiel, Tobias [VerfasserIn] |
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| | Wang, Zhenkun [VerfasserIn] |
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| | Sheet, Debdoot [VerfasserIn] |
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| | Thapa, Shrawan Kumar [VerfasserIn] |
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| | Berniker, Max [VerfasserIn] |
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| | Godau, Patrick [VerfasserIn] |
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| | Morais, Pedro [VerfasserIn] |
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| | Regmi, Sudarshan [VerfasserIn] |
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| | Tran, Thuy [VerfasserIn] |
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| | Fonseca, Jaime [VerfasserIn] |
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| | Nölke, Jan-Hinrich [VerfasserIn] |
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| | Lima, Estevão [VerfasserIn] |
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| | Vazquez, Eduard [VerfasserIn] |
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| | Maier-Hein, Lena [VerfasserIn] |
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| | Navab, Nassir [VerfasserIn] |
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| | Mascagni, Pietro [VerfasserIn] |
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| | Seeliger, Barbara [VerfasserIn] |
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| | Gonzalez, Cristians [VerfasserIn] |
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| | Mutter, Didier [VerfasserIn] |
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| | Padoy, Nicolas [VerfasserIn] |
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| Titel: | CholecTriplet2022 |
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| Titelzusatz: | show me a tool and tell me the triplet - an endoscopic vision challenge for surgical action triplet detection |
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| Verf.angabe: | Chinedu Innocent Nwoye, Tong Yu, Saurav Sharma, Aditya Murali, Deepak Alapatt, Armine Vardazaryan, Kun Yuan, Jonas Hajek, Wolfgang Reiter, Amine Yamlahi, Finn-Henri Smidt, Xiaoyang Zou, Guoyan Zheng, Bruno Oliveira, Helena R. Torres, Satoshi Kondo, Satoshi Kasai, Felix Holm, Ege Özsoy, Shuangchun Gui, Han Li, Sista Raviteja, Rachana Sathish, Pranav Poudel, Binod Bhattarai, Ziheng Wang, Guo Rui, Melanie Schellenberg, João L. Vilaça, Tobias Czempiel, Zhenkun Wang, Debdoot Sheet, Shrawan Kumar Thapa, Max Berniker, Patrick Godau, Pedro Morais, Sudarshan Regmi, Thuy Nuong Tran, Jaime Fonseca, Jan-Hinrich Nölke, Estevão Lima, Eduard Vazquez, Lena Maier-Hein, Nassir Navab, Pietro Mascagni, Barbara Seeliger, Cristians Gonzalez, Didier Mutter, Nicolas Padoy |
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| E-Jahr: | 2023 |
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| Jahr: | 4 July 2023 |
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| Umfang: | 1-21$p21 |
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| Fussnoten: | Gesehen am 27.06.2024 |
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| Titel Quelle: | Enthalten in: Medical image analysis |
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| Ort Quelle: | Amsterdam [u.a.] : Elsevier Science, 1996 |
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| Jahr Quelle: | 2023 |
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| Band/Heft Quelle: | 89(2023) vom: Okt., Artikel-ID 102888 |
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| ISSN Quelle: | 1361-8423 |
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| Abstract: | Formalizing surgical activities as triplets of the used instruments, actions performed, and target anatomies is becoming a gold standard approach for surgical activity modeling. The benefit is that this formalization helps to obtain a more detailed understanding of tool-tissue interaction which can be used to develop better Artificial Intelligence assistance for image-guided surgery. Earlier efforts and the CholecTriplet challenge introduced in 2021 have put together techniques aimed at recognizing these triplets from surgical footage. Estimating also the spatial locations of the triplets would offer a more precise intraoperative context-aware decision support for computer-assisted intervention. This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection. It includes weakly-supervised bounding box localization of every visible surgical instrument (or tool), as the key actors, and the modeling of each tool-activity in the form of ‹instrument, verb, target› triplet. The paper describes a baseline method and 10 new deep learning algorithms presented at the challenge to solve the task. It also provides thorough methodological comparisons of the methods, an in-depth analysis of the obtained results across multiple metrics, visual and procedural challenges; their significance, and useful insights for future research directions and applications in surgery. |
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| DOI: | doi:10.1016/j.media.2023.102888 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
kostenfrei: Volltext: https://doi.org/10.1016/j.media.2023.102888 |
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| | kostenfrei: Volltext: https://www.sciencedirect.com/science/article/pii/S1361841523001482 |
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| | DOI: https://doi.org/10.1016/j.media.2023.102888 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | Action detection |
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| | CholecT50 |
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| | Computer-assisted surgery |
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| | Fine-grained activity recognition |
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| | Surgical action triplet |
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| | Tool localization |
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| | Weak supervision |
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| K10plus-PPN: | 1892346877 |
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| Verknüpfungen: | → Zeitschrift |
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CholecTriplet2022 / Nwoye, Chinedu [VerfasserIn]; 4 July 2023 (Online-Ressource)
