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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Hallie McNamara, Omid Mohareri, Jingpei Lu, Xiaorui Zhang et al.
The Journal of Machine Learning for Biomedical Imaging
Surgical Simulation and Training
article

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

Hallie McNamara, Omid Mohareri, Jingpei Lu, Xiaorui Zhang, Ruixing Liang, Lingbo Jin, Jason Culman, Yuxin Chen, Lirong Shao, Fengyi Jiang, Wenqing Sun, Cong Gao, Adi Chola Venkatesh
article en

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.

The Journal of Machine Learning for Biomedical ImagingVol. 3(MICCAI Open Data 2024-2025)
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)
Good health and well-being
Openalex Percentile: Top 23%
Surgical Simulation and Training
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.