CholecInstanceSeg: A Tool Instance Segmentation Dataset for Laparoscopic Surgery
This provides a high-quality dataset for developing and evaluating tool instance segmentation algorithms in laparoscopic surgery, addressing a gap in existing resources.
The authors tackled the lack of comprehensive tool instance segmentation datasets in laparoscopic surgery by introducing CholecInstanceSeg, the largest open-access dataset with 41.9k annotated frames and 64.4k tool instances from clinical procedures.
In laparoscopic and robotic surgery, precise tool instance segmentation is an essential technology for advanced computer-assisted interventions. Although publicly available procedures of routine surgeries exist, they often lack comprehensive annotations for tool instance segmentation. Additionally, the majority of standard datasets for tool segmentation are derived from porcine(pig) surgeries. To address this gap, we introduce CholecInstanceSeg, the largest open-access tool instance segmentation dataset to date. Derived from the existing CholecT50 and Cholec80 datasets, CholecInstanceSeg provides novel annotations for laparoscopic cholecystectomy procedures in patients. Our dataset comprises 41.9k annotated frames extracted from 85 clinical procedures and 64.4k tool instances, each labelled with semantic masks and instance IDs. To ensure the reliability of our annotations, we perform extensive quality control, conduct label agreement statistics, and benchmark the segmentation results with various instance segmentation baselines. CholecInstanceSeg aims to advance the field by offering a comprehensive and high-quality open-access dataset for the development and evaluation of tool instance segmentation algorithms.