CholecInstanceSeg: A Tool Instance Segmentation Dataset for Laparoscopic Surgery

TL;DR

CholecInstanceSeg is the largest open-access dataset for tool instance segmentation in laparoscopic surgery, providing extensive annotated frames to facilitate development and benchmarking of segmentation algorithms.

Contribution

This paper introduces CholecInstanceSeg, a comprehensive, high-quality dataset for tool instance segmentation in laparoscopic cholecystectomy, filling a gap in publicly available surgical datasets.

Findings

Extensive annotations for 41.9k frames and 64.4k tool instances.

Benchmark results with various segmentation models.

High annotation quality verified through quality control.

Abstract

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.