构建肠镜影像语义分割数据集，助力内窥镜导航与深度感知
SegCol Challenge: Semantic Segmentation for Tools and Fold Edges in Colonoscopy data
- 基于96段肠镜视频，标注工具与皱褶边缘的像素级语义标签
- 提供皱褶边缘作为解剖标志和深度不连续信息，提升定位精度
- 面向医疗影像算法研究者，尤其关注内窥镜导航与三维感知
结直肠癌（CRC）仍是全球癌症致死的主要原因，息肉切除是有效的早期筛查手段。然而，为彻底检测息肉而进行的肠镜导航面临巨大挑战。为推动肠镜中相机导航技术的发展，我们提出了肠镜中工具与皱褶边缘语义分割挑战（SegCol Challenge）。该挑战基于EndoMapper数据仓库，构建了一个包含96段肠镜视频精选帧的标注数据集，对肠皱褶与内窥工具进行人工逐像素语义标注。通过提供皱褶边缘作为解剖地标及工具与皱褶标签所蕴含的深度不连续信息，旨在提升深度感知与定位方法性能。本挑战作为MICCAI 2024 Endovis挑战赛的一部分，致力于推动肠镜导航系统创新。详情请见 https://www.synapse.org/Synapse:syn54124209/wiki/626563，代码资源见 https://github.com/surgical-vision/segcol_challenge。
原文摘要 · Abstract (English)
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 .
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