CholecSeg8k: A Semantic Segmentation Dataset for Laparoscopic Cholecystectomy Based on Cholec80
This dataset addresses the problem of limited annotated data for developing and training computer-assisted surgery algorithms, particularly for semantic segmentation, which is foundational for mechanisms like SLAM.
This paper introduces CholecSeg8k, a new dataset for semantic segmentation in laparoscopic cholecystectomy. It comprises 8,080 pixel-level annotated image frames from 17 video clips of the existing Cholec80 dataset, categorizing pixels into thirteen common surgical classes.
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.