SurgiSR4K: A High‑Resolution Endoscopic Video Dataset for Robotic-Assisted Minimally Invasive Procedures

Fengyi Jiang1Orcid, Xiaorui Zhang1Orcid, Lingbo Jin1Orcid, Ruixing Liang1,2,3, Yuxin Chen1,4Orcid, Adi Chola Venkatesh1Orcid, Jason Culman1, Tiantian Wu5Orcid, Lirong Shao1, Wenqing Sun1, Cong Gao1Orcid, Hallie McNamara1, Jingpei Lu1Orcid, Omid Mohareri1
1: Intuitive Surgical, Inc., Sunnyvale, CA, USA, 2: Johns Hopkins Medicine Neurosurgery, Baltimore, MD, USA, 3: Johns Hopkins University Electrical and Computer Engineering, Baltimore, MD, USA, 4: University of British Columbia Electrical and Computer Engineering, Vancouver, BC, Canada, 5: Wilford & Kate Bailey Small Animal Teaching Hospital, Auburn, AL, USA
Publication date: 2025/12/31
https://doi.org/10.59275/j.melba.2025-f593
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Abstract

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.

Keywords

Endoscopy · Surgical Robotics · Super‑Resolution · Segmentation · Depth Estimation · Tool Tracking · 3D Tissue Reconstruction · Monocular Depth Estimation

Bibtex @article{melba:2025:043:jiang, title = "SurgiSR4K: A High‑Resolution Endoscopic Video Dataset for Robotic-Assisted Minimally Invasive Procedures", author = "Jiang, Fengyi and Zhang, Xiaorui and Jin, Lingbo and Liang, Ruixing and Chen, Yuxin and Venkatesh, Adi Chola and Culman, Jason and Wu, Tiantian and Shao, Lirong and Sun, Wenqing and Gao, Cong and McNamara, Hallie and Lu, Jingpei and Mohareri, Omid", journal = "Machine Learning for Biomedical Imaging", volume = "3", issue = "Special Issue on Open Data at MICCAI 2024–2025", year = "2025", pages = "875--885", issn = "2766-905X", doi = "https://doi.org/10.59275/j.melba.2025-f593", url = "https://melba-journal.org/2025:043" }
RISTY - JOUR AU - Jiang, Fengyi AU - Zhang, Xiaorui AU - Jin, Lingbo AU - Liang, Ruixing AU - Chen, Yuxin AU - Venkatesh, Adi Chola AU - Culman, Jason AU - Wu, Tiantian AU - Shao, Lirong AU - Sun, Wenqing AU - Gao, Cong AU - McNamara, Hallie AU - Lu, Jingpei AU - Mohareri, Omid PY - 2025 TI - SurgiSR4K: A High‑Resolution Endoscopic Video Dataset for Robotic-Assisted Minimally Invasive Procedures T2 - Machine Learning for Biomedical Imaging VL - 3 IS - Special Issue on Open Data at MICCAI 2024–2025 SP - 875 EP - 885 SN - 2766-905X DO - https://doi.org/10.59275/j.melba.2025-f593 UR - https://melba-journal.org/2025:043 ER -

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