Year: 2025
Type: article
Abstract: Egocentric open-surgery videos capture rich, fine-grained details essential for accurately modelling surgical procedures and human behaviour in the operating room. A detailed, pixel-level understanding of hands and surgical tools is crucial for interpreting a surgeon action and intention. We introduce EgoSurgery-HTS, a new dataset with pixel-wise annotations and a benchmark suite for segmenting surgical tools, hands, and interacting tools in egocentric open-surgery videos. Specifically, we provide a labelled dataset for (1) tool instance segmentation of 14 distinct surgical tools, (2) hand instance segmentation, and (3) hand-tool segmentation to label main operating hands and the tools they manipulate. Using EgoSurgery-HTS, we conduct extensive evaluations of state-of-the-art segmentation methods and demonstrate significant improvements in the accuracy of hand and hand-tool segmentation in egocentric open-surgery videos compared to existing datasets. The dataset will be released upon acceptance.
Source: Healthcare Technology Letters
DOI: 10.1049/htl2.70049
Institution: Keio University
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,500
OpenAlex ID: W4417274591