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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.07471 |
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| _version_ | 1866914876831563776 |
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| author | Hu, Ming Xia, Peng Wang, Lin Yan, Siyuan Tang, Feilong Xu, Zhongxing Luo, Yimin Song, Kaimin Leitner, Jurgen Cheng, Xuelian Cheng, Jun Liu, Chi Zhou, Kaijing Ge, Zongyuan |
| author_facet | Hu, Ming Xia, Peng Wang, Lin Yan, Siyuan Tang, Feilong Xu, Zhongxing Luo, Yimin Song, Kaimin Leitner, Jurgen Cheng, Xuelian Cheng, Jun Liu, Chi Zhou, Kaijing Ge, Zongyuan |
| contents | Surgical scene perception via videos is critical for advancing robotic surgery, telesurgery, and AI-assisted surgery, particularly in ophthalmology. However, the scarcity of diverse and richly annotated video datasets has hindered the development of intelligent systems for surgical workflow analysis. Existing datasets face challenges such as small scale, lack of diversity in surgery and phase categories, and absence of time-localized annotations. These limitations impede action understanding and model generalization validation in complex and diverse real-world surgical scenarios. To address this gap, we introduce OphNet, a large-scale, expert-annotated video benchmark for ophthalmic surgical workflow understanding. OphNet features: 1) A diverse collection of 2,278 surgical videos spanning 66 types of cataract, glaucoma, and corneal surgeries, with detailed annotations for 102 unique surgical phases and 150 fine-grained operations. 2) Sequential and hierarchical annotations for each surgery, phase, and operation, enabling comprehensive understanding and improved interpretability. 3) Time-localized annotations, facilitating temporal localization and prediction tasks within surgical workflows. With approximately 285 hours of surgical videos, OphNet is about 20 times larger than the largest existing surgical workflow analysis benchmark. Code and dataset are available at: https://minghu0830.github.io/OphNet-benchmark/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_07471 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | OphNet: A Large-Scale Video Benchmark for Ophthalmic Surgical Workflow Understanding Hu, Ming Xia, Peng Wang, Lin Yan, Siyuan Tang, Feilong Xu, Zhongxing Luo, Yimin Song, Kaimin Leitner, Jurgen Cheng, Xuelian Cheng, Jun Liu, Chi Zhou, Kaijing Ge, Zongyuan Computer Vision and Pattern Recognition Surgical scene perception via videos is critical for advancing robotic surgery, telesurgery, and AI-assisted surgery, particularly in ophthalmology. However, the scarcity of diverse and richly annotated video datasets has hindered the development of intelligent systems for surgical workflow analysis. Existing datasets face challenges such as small scale, lack of diversity in surgery and phase categories, and absence of time-localized annotations. These limitations impede action understanding and model generalization validation in complex and diverse real-world surgical scenarios. To address this gap, we introduce OphNet, a large-scale, expert-annotated video benchmark for ophthalmic surgical workflow understanding. OphNet features: 1) A diverse collection of 2,278 surgical videos spanning 66 types of cataract, glaucoma, and corneal surgeries, with detailed annotations for 102 unique surgical phases and 150 fine-grained operations. 2) Sequential and hierarchical annotations for each surgery, phase, and operation, enabling comprehensive understanding and improved interpretability. 3) Time-localized annotations, facilitating temporal localization and prediction tasks within surgical workflows. With approximately 285 hours of surgical videos, OphNet is about 20 times larger than the largest existing surgical workflow analysis benchmark. Code and dataset are available at: https://minghu0830.github.io/OphNet-benchmark/. |
| title | OphNet: A Large-Scale Video Benchmark for Ophthalmic Surgical Workflow Understanding |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.07471 |