CholecSeg8k: A Semantic Segmentation Dataset for Laparoscopic Cholecystectomy Based on Cholec80

TL;DR

This paper introduces CholecSeg8k, a new large-scale, pixel-annotated dataset for semantic segmentation in laparoscopic cholecystectomy, aiming to advance computer-assisted surgery algorithms.

Contribution

It provides a comprehensive, annotated dataset based on Cholec80, enabling improved development of algorithms like SLAM for surgical applications.

Findings

Dataset contains 8,080 annotated images

Includes 13 classes relevant to laparoscopic cholecystectomy

Released under open license for research use

Abstract

Computer-assisted surgery has been developed to enhance surgery correctness and safety. However, researchers and engineers suffer from limited annotated data to develop and train better algorithms. Consequently, the development of fundamental algorithms such as Simultaneous Localization and Mapping (SLAM) is limited. This article elaborates on the efforts of preparing the dataset for semantic segmentation, which is the foundation of many computer-assisted surgery mechanisms. Based on the Cholec80 dataset [3], we extracted 8,080 laparoscopic cholecystectomy image frames from 17 video clips in Cholec80 and annotated the images. The dataset is named CholecSeg8K and its total size is 3GB. Each of these images is annotated at pixel-level for thirteen classes, which are commonly founded in laparoscopic cholecystectomy surgery. CholecSeg8k is released under the license CC BY- NC-SA 4.0.