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

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arxiv 2012.12453 v1 pith:ROMUH4MF submitted 2020-12-23 cs.CV

W.-Y. Hong , C.-L. Kao , Y.-H. Kuo , J.-R. Wang , W.-L. Chang , C.-S. Shih This is my paper
classification cs.CV
keywords datasetsurgeryannotatedcholec80cholecseg8kcholecystectomylaparoscopicalgorithms
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 43 citations worldwide. Full citation record

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