SurgiSR4K: A High‑Resolution Endoscopic Video Dataset for Robotic-Assisted Minimally Invasive Procedures
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.
Authors
- Hallie McNamara
- Omid Mohareri
- Jingpei Lu (ORCID: https://orcid.org/0000-0002-9136-6096)
- Xiaorui Zhang (ORCID: https://orcid.org/0000-0002-7030-6529)
- Ruixing Liang
- Lingbo Jin
- Jason Culman
- Yuxin Chen
- Lirong Shao
- Fengyi Jiang
- Wenqing Sun
- Cong Gao
- Adi Chola Venkatesh
Institutions
- University of British Columbia (CA)
- Johns Hopkins University (US)
- Johns Hopkins Medicine (US)
- Johns Hopkins Hospital (US)
- Intuitive Surgical (United States) (US)
- Auburn University (US)
Publication Details
- Journal
- The Journal of Machine Learning for Biomedical Imaging
- Published
- 2025-12-31
- DOI
- https://doi.org/10.59275/j.melba.2025-f593
- Primary Topic
- Surgical Simulation and Training
- Type
- article
- Field-Weighted Citation Impact
- 0.00