Scholarlink

Colon polyp screening is critical for the prevention of colon cancer, and the use of colon polyp segmentation to assist physicians in identifying potential polyps can improve detection efficiency and reduce misdiagnosis and missed diagnoses. However, polyp segmentation encounters the following challenges: (1) the size and shape of polyps vary widely; (2) the edge between polyps and the surrounding normal area is not obvious. To address the above challenges, a novel colon polyp segmentation method like human observation (LHONet) is proposed. This approach aims to align the polyp segmentation network more closely with human cognitive processes. First, the rough outline of the colon polyp image is identified to roughly understand the size and shape of the polyp, and then the polyp edge is finely segmented. The network structure consists of three modules: the Rough Outline Generation (ROG) module is designed to generate the rough outline of colon polyps; the Edge Information Extraction (EIE) module extracts the edge information of the polyps more accurately by combining with the classical edge detection technique; and the Outline Feature Clarifying (OFC) module is devised to supplement the edge information into the rough outline to realize the accurate segmentation of polyps. The method was compared to other methods on five datasets: EndoScene, CVC-ClinicDB, KvasirSEG, CVC-ColonDB, and ETIS-LaribPolypDB, with mDice scores of 90.79, 94.46, 92.13, 82.00, 82.29%, respectively. The codes are available at https://github.com/heyeying/LHONet .

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

From Coarse to Fine: A Novel Colon Polyp Segmentation Method Like Human Observation

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Wei Wang,
  • Huiying Sun,
  • Xin Wang

摘要

Colon polyp screening is critical for the prevention of colon cancer, and the use of colon polyp segmentation to assist physicians in identifying potential polyps can improve detection efficiency and reduce misdiagnosis and missed diagnoses. However, polyp segmentation encounters the following challenges: (1) the size and shape of polyps vary widely; (2) the edge between polyps and the surrounding normal area is not obvious. To address the above challenges, a novel colon polyp segmentation method like human observation (LHONet) is proposed. This approach aims to align the polyp segmentation network more closely with human cognitive processes. First, the rough outline of the colon polyp image is identified to roughly understand the size and shape of the polyp, and then the polyp edge is finely segmented. The network structure consists of three modules: the Rough Outline Generation (ROG) module is designed to generate the rough outline of colon polyps; the Edge Information Extraction (EIE) module extracts the edge information of the polyps more accurately by combining with the classical edge detection technique; and the Outline Feature Clarifying (OFC) module is devised to supplement the edge information into the rough outline to realize the accurate segmentation of polyps. The method was compared to other methods on five datasets: EndoScene, CVC-ClinicDB, KvasirSEG, CVC-ColonDB, and ETIS-LaribPolypDB, with mDice scores of 90.79, 94.46, 92.13, 82.00, 82.29%, respectively. The codes are available at https://github.com/heyeying/LHONet .