SurgiSR4K: A High‑Resolution Endoscopic Video Dataset for Robotic-Assisted Minimally Invasive Procedures
Fengyi Jiang1
, Xiaorui Zhang1
, Lingbo Jin1
, Ruixing Liang1,2,3, Yuxin Chen1,4
, Adi Chola Venkatesh1
, Jason Culman1, Tiantian Wu5
, Lirong Shao1, Wenqing Sun1, Cong Gao1
, Hallie McNamara1, Jingpei Lu1
, 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
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"
}
RIS
TY - 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 -