SegCol Challenge: Semantic Segmentation for Tools and Fold Edges in Colonoscopy data
This addresses the problem of thorough polyp detection during colonoscopy for medical professionals, but it is incremental as it builds on existing datasets and challenges.
The paper tackles the challenge of camera navigation in colonoscopy for colorectal cancer screening by introducing the SegCol Challenge, which provides a dataset with pixel-level semantic labels for colon folds and endoscopic tools from 96 colonoscopy videos to improve depth perception and localization methods.
Colorectal cancer (CRC) remains a leading cause of cancer-related deaths worldwide, with polyp removal being an effective early screening method. However, navigating the colon for thorough polyp detection poses significant challenges. To advance camera navigation in colonoscopy, we propose the Semantic Segmentation for Tools and Fold Edges in Colonoscopy (SegCol) Challenge. This challenge introduces a dataset from the EndoMapper repository, featuring manually annotated, pixel-level semantic labels for colon folds and endoscopic tools across selected frames from 96 colonoscopy videos. By providing fold edges as anatomical landmarks and depth discontinuity information from both fold and tool labels, the dataset is aimed to improve depth perception and localization methods. Hosted as part of the Endovis Challenge at MICCAI 2024, SegCol aims to drive innovation in colonoscopy navigation systems. Details are available at https://www.synapse.org/Synapse:syn54124209/wiki/626563, and code resources at https://github.com/surgical-vision/segcol_challenge .