  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.
