CathAction: A Benchmark for Endovascular Intervention Understanding
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866917763303342080 |
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| author | Huang, Baoru Vo, Tuan Kongtongvattana, Chayun Dagnino, Giulio Kundrat, Dennis Chi, Wenqiang Abdelaziz, Mohamed Kwok, Trevor Jianu, Tudor Do, Tuong Le, Hieu Nguyen, Minh Nguyen, Hoan Tjiputra, Erman Tran, Quang Xie, Jianyang Meng, Yanda Bhattarai, Binod Tan, Zhaorui Liu, Hongbin Gan, Hong Seng Wang, Wei Yang, Xi Wang, Qiufeng Su, Jionglong Huang, Kaizhu Stefanidis, Angelos Guo, Min Du, Bo Tao, Rong Vu, Minh Zheng, Guoyan Zheng, Yalin Vasconcelos, Francisco Stoyanov, Danail Elson, Daniel Baena, Ferdinando Rodriguez y Nguyen, Anh |
| author_facet | Huang, Baoru Vo, Tuan Kongtongvattana, Chayun Dagnino, Giulio Kundrat, Dennis Chi, Wenqiang Abdelaziz, Mohamed Kwok, Trevor Jianu, Tudor Do, Tuong Le, Hieu Nguyen, Minh Nguyen, Hoan Tjiputra, Erman Tran, Quang Xie, Jianyang Meng, Yanda Bhattarai, Binod Tan, Zhaorui Liu, Hongbin Gan, Hong Seng Wang, Wei Yang, Xi Wang, Qiufeng Su, Jionglong Huang, Kaizhu Stefanidis, Angelos Guo, Min Du, Bo Tao, Rong Vu, Minh Zheng, Guoyan Zheng, Yalin Vasconcelos, Francisco Stoyanov, Danail Elson, Daniel Baena, Ferdinando Rodriguez y Nguyen, Anh |
| contents | Real-time visual feedback from catheterization analysis is crucial for enhancing surgical safety and efficiency during endovascular interventions. However, existing datasets are often limited to specific tasks, small scale, and lack the comprehensive annotations necessary for broader endovascular intervention understanding. To tackle these limitations, we introduce CathAction, a large-scale dataset for catheterization understanding. Our CathAction dataset encompasses approximately 500,000 annotated frames for catheterization action understanding and collision detection, and 25,000 ground truth masks for catheter and guidewire segmentation. For each task, we benchmark recent related works in the field. We further discuss the challenges of endovascular intentions compared to traditional computer vision tasks and point out open research questions. We hope that CathAction will facilitate the development of endovascular intervention understanding methods that can be applied to real-world applications. The dataset is available at https://airvlab.github.io/cathaction/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_13126 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CathAction: A Benchmark for Endovascular Intervention Understanding Huang, Baoru Vo, Tuan Kongtongvattana, Chayun Dagnino, Giulio Kundrat, Dennis Chi, Wenqiang Abdelaziz, Mohamed Kwok, Trevor Jianu, Tudor Do, Tuong Le, Hieu Nguyen, Minh Nguyen, Hoan Tjiputra, Erman Tran, Quang Xie, Jianyang Meng, Yanda Bhattarai, Binod Tan, Zhaorui Liu, Hongbin Gan, Hong Seng Wang, Wei Yang, Xi Wang, Qiufeng Su, Jionglong Huang, Kaizhu Stefanidis, Angelos Guo, Min Du, Bo Tao, Rong Vu, Minh Zheng, Guoyan Zheng, Yalin Vasconcelos, Francisco Stoyanov, Danail Elson, Daniel Baena, Ferdinando Rodriguez y Nguyen, Anh Computer Vision and Pattern Recognition Real-time visual feedback from catheterization analysis is crucial for enhancing surgical safety and efficiency during endovascular interventions. However, existing datasets are often limited to specific tasks, small scale, and lack the comprehensive annotations necessary for broader endovascular intervention understanding. To tackle these limitations, we introduce CathAction, a large-scale dataset for catheterization understanding. Our CathAction dataset encompasses approximately 500,000 annotated frames for catheterization action understanding and collision detection, and 25,000 ground truth masks for catheter and guidewire segmentation. For each task, we benchmark recent related works in the field. We further discuss the challenges of endovascular intentions compared to traditional computer vision tasks and point out open research questions. We hope that CathAction will facilitate the development of endovascular intervention understanding methods that can be applied to real-world applications. The dataset is available at https://airvlab.github.io/cathaction/. |
| title | CathAction: A Benchmark for Endovascular Intervention Understanding |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.13126 |