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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2507.16559 |
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| _version_ | 1866918316711346176 |
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| author | Rueckert, Tobias Rauber, David Maerkl, Raphaela Klausmann, Leonard Yildiran, Suemeyye R. Gutbrod, Max Nunes, Danilo Weber Moreno, Alvaro Fernandez Luengo, Imanol Stoyanov, Danail Toussaint, Nicolas Cho, Enki Kim, Hyeon Bae Choo, Oh Sung Kim, Ka Young Kim, Seong Tae Arantes, Gonçalo Song, Kehan Zhu, Jianjun Xiong, Junchen Lin, Tingyi Kikuchi, Shunsuke Matsuzaki, Hiroki Kouno, Atsushi Manesco, João Renato Ribeiro Papa, João Paulo Choi, Tae-Min Jeong, Tae Kyeong Park, Juyoun Alabi, Oluwatosin Wei, Meng Vercauteren, Tom Wu, Runzhi Xu, Mengya Wang, An Bai, Long Ren, Hongliang Yamlahi, Amine Hennighausen, Jakob Maier-Hein, Lena Kondo, Satoshi Kasai, Satoshi Hirasawa, Kousuke Yang, Shu Wang, Yihui Chen, Hao Rodríguez, Santiago Aparicio, Nicolás Manrique, Leonardo Lyons, Juan Camilo Hosie, Olivia Ayobi, Nicolás Arbeláez, Pablo Li, Yiping Khalil, Yasmina Al Nasirihaghighi, Sahar Speidel, Stefanie Rueckert, Daniel Feussner, Hubertus Wilhelm, Dirk Palm, Christoph |
| author_facet | Rueckert, Tobias Rauber, David Maerkl, Raphaela Klausmann, Leonard Yildiran, Suemeyye R. Gutbrod, Max Nunes, Danilo Weber Moreno, Alvaro Fernandez Luengo, Imanol Stoyanov, Danail Toussaint, Nicolas Cho, Enki Kim, Hyeon Bae Choo, Oh Sung Kim, Ka Young Kim, Seong Tae Arantes, Gonçalo Song, Kehan Zhu, Jianjun Xiong, Junchen Lin, Tingyi Kikuchi, Shunsuke Matsuzaki, Hiroki Kouno, Atsushi Manesco, João Renato Ribeiro Papa, João Paulo Choi, Tae-Min Jeong, Tae Kyeong Park, Juyoun Alabi, Oluwatosin Wei, Meng Vercauteren, Tom Wu, Runzhi Xu, Mengya Wang, An Bai, Long Ren, Hongliang Yamlahi, Amine Hennighausen, Jakob Maier-Hein, Lena Kondo, Satoshi Kasai, Satoshi Hirasawa, Kousuke Yang, Shu Wang, Yihui Chen, Hao Rodríguez, Santiago Aparicio, Nicolás Manrique, Leonardo Lyons, Juan Camilo Hosie, Olivia Ayobi, Nicolás Arbeláez, Pablo Li, Yiping Khalil, Yasmina Al Nasirihaghighi, Sahar Speidel, Stefanie Rueckert, Daniel Feussner, Hubertus Wilhelm, Dirk Palm, Christoph |
| contents | Reliable recognition and localization of surgical instruments in endoscopic video recordings are foundational for a wide range of applications in computer- and robot-assisted minimally invasive surgery (RAMIS), including surgical training, skill assessment, and autonomous assistance. However, robust performance under real-world conditions remains a significant challenge. Incorporating surgical context - such as the current procedural phase - has emerged as a promising strategy to improve robustness and interpretability.
To address these challenges, we organized the Surgical Procedure Phase, Keypoint, and Instrument Recognition (PhaKIR) sub-challenge as part of the Endoscopic Vision (EndoVis) challenge at MICCAI 2024. We introduced a novel, multi-center dataset comprising thirteen full-length laparoscopic cholecystectomy videos collected from three distinct medical institutions, with unified annotations for three interrelated tasks: surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation. Unlike existing datasets, ours enables joint investigation of instrument localization and procedural context within the same data while supporting the integration of temporal information across entire procedures.
We report results and findings in accordance with the BIAS guidelines for biomedical image analysis challenges. The PhaKIR sub-challenge advances the field by providing a unique benchmark for developing temporally aware, context-driven methods in RAMIS and offers a high-quality resource to support future research in surgical scene understanding. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16559 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge Rueckert, Tobias Rauber, David Maerkl, Raphaela Klausmann, Leonard Yildiran, Suemeyye R. Gutbrod, Max Nunes, Danilo Weber Moreno, Alvaro Fernandez Luengo, Imanol Stoyanov, Danail Toussaint, Nicolas Cho, Enki Kim, Hyeon Bae Choo, Oh Sung Kim, Ka Young Kim, Seong Tae Arantes, Gonçalo Song, Kehan Zhu, Jianjun Xiong, Junchen Lin, Tingyi Kikuchi, Shunsuke Matsuzaki, Hiroki Kouno, Atsushi Manesco, João Renato Ribeiro Papa, João Paulo Choi, Tae-Min Jeong, Tae Kyeong Park, Juyoun Alabi, Oluwatosin Wei, Meng Vercauteren, Tom Wu, Runzhi Xu, Mengya Wang, An Bai, Long Ren, Hongliang Yamlahi, Amine Hennighausen, Jakob Maier-Hein, Lena Kondo, Satoshi Kasai, Satoshi Hirasawa, Kousuke Yang, Shu Wang, Yihui Chen, Hao Rodríguez, Santiago Aparicio, Nicolás Manrique, Leonardo Lyons, Juan Camilo Hosie, Olivia Ayobi, Nicolás Arbeláez, Pablo Li, Yiping Khalil, Yasmina Al Nasirihaghighi, Sahar Speidel, Stefanie Rueckert, Daniel Feussner, Hubertus Wilhelm, Dirk Palm, Christoph Computer Vision and Pattern Recognition Reliable recognition and localization of surgical instruments in endoscopic video recordings are foundational for a wide range of applications in computer- and robot-assisted minimally invasive surgery (RAMIS), including surgical training, skill assessment, and autonomous assistance. However, robust performance under real-world conditions remains a significant challenge. Incorporating surgical context - such as the current procedural phase - has emerged as a promising strategy to improve robustness and interpretability. To address these challenges, we organized the Surgical Procedure Phase, Keypoint, and Instrument Recognition (PhaKIR) sub-challenge as part of the Endoscopic Vision (EndoVis) challenge at MICCAI 2024. We introduced a novel, multi-center dataset comprising thirteen full-length laparoscopic cholecystectomy videos collected from three distinct medical institutions, with unified annotations for three interrelated tasks: surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation. Unlike existing datasets, ours enables joint investigation of instrument localization and procedural context within the same data while supporting the integration of temporal information across entire procedures. We report results and findings in accordance with the BIAS guidelines for biomedical image analysis challenges. The PhaKIR sub-challenge advances the field by providing a unique benchmark for developing temporally aware, context-driven methods in RAMIS and offers a high-quality resource to support future research in surgical scene understanding. |
| title | Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.16559 |