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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2503.24306 |
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| _version_ | 1866916667996504064 |
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| author | Schmidt, Adam Karaoglu, Mert Asim Sinha, Soham Jang, Mingang Ha, Ho-Gun Jung, Kyungmin Gu, Kyeongmo Ullah, Ihsan Lee, Hyunki Šerých, Jonáš Neoral, Michal Matas, Jiří Zhou, Rulin He, Wenlong Wang, An Ren, Hongliang Silva, Bruno Queirós, Sandro Lima, Estêvão Vilaça, João L. Kikuchi, Shunsuke Kouno, Atsushi Matsuzaki, Hiroki Li, Tongtong Chen, Yulu Li, Ling Ma, Xiang Li, Xiaojian Zeinoddin, Mona Sheikh Wang, Xu Tandogdu, Zafer Shaw, Greg Mazomenos, Evangelos Stoyanov, Danail Chen, Yuxin Wu, Zijian Ladikos, Alexander DiMaio, Simon Salcudean, Septimiu E. Mohareri, Omid |
| author_facet | Schmidt, Adam Karaoglu, Mert Asim Sinha, Soham Jang, Mingang Ha, Ho-Gun Jung, Kyungmin Gu, Kyeongmo Ullah, Ihsan Lee, Hyunki Šerých, Jonáš Neoral, Michal Matas, Jiří Zhou, Rulin He, Wenlong Wang, An Ren, Hongliang Silva, Bruno Queirós, Sandro Lima, Estêvão Vilaça, João L. Kikuchi, Shunsuke Kouno, Atsushi Matsuzaki, Hiroki Li, Tongtong Chen, Yulu Li, Ling Ma, Xiang Li, Xiaojian Zeinoddin, Mona Sheikh Wang, Xu Tandogdu, Zafer Shaw, Greg Mazomenos, Evangelos Stoyanov, Danail Chen, Yuxin Wu, Zijian Ladikos, Alexander DiMaio, Simon Salcudean, Septimiu E. Mohareri, Omid |
| contents | Understanding tissue motion in surgery is crucial to enable applications in downstream tasks such as segmentation, 3D reconstruction, virtual tissue landmarking, autonomous probe-based scanning, and subtask autonomy. Labeled data are essential to enabling algorithms in these downstream tasks since they allow us to quantify and train algorithms. This paper introduces a point tracking challenge to address this, wherein participants can submit their algorithms for quantification. The submitted algorithms are evaluated using a dataset named surgical tattoos in infrared (STIR), with the challenge aptly named the STIR Challenge 2024. The STIR Challenge 2024 comprises two quantitative components: accuracy and efficiency. The accuracy component tests the accuracy of algorithms on in vivo and ex vivo sequences. The efficiency component tests the latency of algorithm inference. The challenge was conducted as a part of MICCAI EndoVis 2024. In this challenge, we had 8 total teams, with 4 teams submitting before and 4 submitting after challenge day. This paper details the STIR Challenge 2024, which serves to move the field towards more accurate and efficient algorithms for spatial understanding in surgery. In this paper we summarize the design, submissions, and results from the challenge. The challenge dataset is available here: https://zenodo.org/records/14803158 , and the code for baseline models and metric calculation is available here: https://github.com/athaddius/STIRMetrics |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_24306 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Point Tracking in Surgery--The 2024 Surgical Tattoos in Infrared (STIR) Challenge Schmidt, Adam Karaoglu, Mert Asim Sinha, Soham Jang, Mingang Ha, Ho-Gun Jung, Kyungmin Gu, Kyeongmo Ullah, Ihsan Lee, Hyunki Šerých, Jonáš Neoral, Michal Matas, Jiří Zhou, Rulin He, Wenlong Wang, An Ren, Hongliang Silva, Bruno Queirós, Sandro Lima, Estêvão Vilaça, João L. Kikuchi, Shunsuke Kouno, Atsushi Matsuzaki, Hiroki Li, Tongtong Chen, Yulu Li, Ling Ma, Xiang Li, Xiaojian Zeinoddin, Mona Sheikh Wang, Xu Tandogdu, Zafer Shaw, Greg Mazomenos, Evangelos Stoyanov, Danail Chen, Yuxin Wu, Zijian Ladikos, Alexander DiMaio, Simon Salcudean, Septimiu E. Mohareri, Omid Computer Vision and Pattern Recognition Understanding tissue motion in surgery is crucial to enable applications in downstream tasks such as segmentation, 3D reconstruction, virtual tissue landmarking, autonomous probe-based scanning, and subtask autonomy. Labeled data are essential to enabling algorithms in these downstream tasks since they allow us to quantify and train algorithms. This paper introduces a point tracking challenge to address this, wherein participants can submit their algorithms for quantification. The submitted algorithms are evaluated using a dataset named surgical tattoos in infrared (STIR), with the challenge aptly named the STIR Challenge 2024. The STIR Challenge 2024 comprises two quantitative components: accuracy and efficiency. The accuracy component tests the accuracy of algorithms on in vivo and ex vivo sequences. The efficiency component tests the latency of algorithm inference. The challenge was conducted as a part of MICCAI EndoVis 2024. In this challenge, we had 8 total teams, with 4 teams submitting before and 4 submitting after challenge day. This paper details the STIR Challenge 2024, which serves to move the field towards more accurate and efficient algorithms for spatial understanding in surgery. In this paper we summarize the design, submissions, and results from the challenge. The challenge dataset is available here: https://zenodo.org/records/14803158 , and the code for baseline models and metric calculation is available here: https://github.com/athaddius/STIRMetrics |
| title | Point Tracking in Surgery--The 2024 Surgical Tattoos in Infrared (STIR) Challenge |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.24306 |