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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.00209 |
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| _version_ | 1866911631299051520 |
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| author | Jiang, Fengyi Zhang, Xiaorui Jin, Lingbo Liang, Ruixing Chen, Yuxin Venkatesh, Adi Chola Culman, Jason Wu, Tiantian Shao, Lirong Sun, Wenqing Gao, Cong McNamara, Hallie Lu, Jingpei Mohareri, Omid |
| author_facet | Jiang, Fengyi Zhang, Xiaorui Jin, Lingbo Liang, Ruixing Chen, Yuxin Venkatesh, Adi Chola Culman, Jason Wu, Tiantian Shao, Lirong Sun, Wenqing Gao, Cong McNamara, Hallie Lu, Jingpei Mohareri, Omid |
| contents | High-resolution imaging is crucial for enhancing visual clarity and enabling precise computer-assisted guidance in minimally invasive surgery (MIS). Despite the increasing adoption of 4K endoscopic systems, there remains a significant gap in publicly available native 4K datasets tailored specifically for robotic-assisted MIS. We introduce SurgiSR4K, the first publicly accessible surgical imaging and video dataset captured at a native 4K resolution, representing realistic conditions of robotic-assisted procedures. SurgiSR4K comprises diverse visual scenarios including specular reflections, tool occlusions, bleeding, and soft tissue deformations, meticulously designed to reflect common challenges faced during laparoscopic and robotic surgeries. This dataset opens up possibilities for a broad range of computer vision tasks that might benefit from high resolution data, such as super resolution (SR), smoke removal, surgical instrument detection, 3D tissue reconstruction, monocular depth estimation, instance segmentation, novel view synthesis, and vision-language model (VLM) development. SurgiSR4K provides a robust foundation for advancing research in high-resolution surgical imaging and fosters the development of intelligent imaging technologies aimed at enhancing performance, safety, and usability in image-guided robotic surgeries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_00209 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SurgiSR4K: A High-Resolution Endoscopic Video Dataset for Robotic-Assisted Minimally Invasive Procedures Jiang, Fengyi Zhang, Xiaorui Jin, Lingbo Liang, Ruixing Chen, Yuxin Venkatesh, Adi Chola Culman, Jason Wu, Tiantian Shao, Lirong Sun, Wenqing Gao, Cong McNamara, Hallie Lu, Jingpei Mohareri, Omid Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Robotics High-resolution imaging is crucial for enhancing visual clarity and enabling precise computer-assisted guidance in minimally invasive surgery (MIS). Despite the increasing adoption of 4K endoscopic systems, there remains a significant gap in publicly available native 4K datasets tailored specifically for robotic-assisted MIS. We introduce SurgiSR4K, the first publicly accessible surgical imaging and video dataset captured at a native 4K resolution, representing realistic conditions of robotic-assisted procedures. SurgiSR4K comprises diverse visual scenarios including specular reflections, tool occlusions, bleeding, and soft tissue deformations, meticulously designed to reflect common challenges faced during laparoscopic and robotic surgeries. This dataset opens up possibilities for a broad range of computer vision tasks that might benefit from high resolution data, such as super resolution (SR), smoke removal, surgical instrument detection, 3D tissue reconstruction, monocular depth estimation, instance segmentation, novel view synthesis, and vision-language model (VLM) development. SurgiSR4K provides a robust foundation for advancing research in high-resolution surgical imaging and fosters the development of intelligent imaging technologies aimed at enhancing performance, safety, and usability in image-guided robotic surgeries. |
| title | SurgiSR4K: A High-Resolution Endoscopic Video Dataset for Robotic-Assisted Minimally Invasive Procedures |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2507.00209 |