Your browser doesn't support javascript.
loading
Polyp segmentation based on implicit edge-guided cross-layer fusion networks.
Liu, Junqing; Zhang, Weiwei; Liu, Yong; Zhang, Qinghe.
Afiliación
  • Liu J; Hubei Engineering and Technology Research Center for Construction Quality Inspection Equipment, China Three Gorges University, Yichang, 443002, Hubei, People's Republic of China.
  • Zhang W; College of Computer and Information Technology, China Three Gorges University, Yichang, 443002, Hubei, People's Republic of China.
  • Liu Y; Hubei Engineering and Technology Research Center for Construction Quality Inspection Equipment, China Three Gorges University, Yichang, 443002, Hubei, People's Republic of China. youn_xxt@163.com.
  • Zhang Q; College of Computer and Information Technology, China Three Gorges University, Yichang, 443002, Hubei, People's Republic of China. youn_xxt@163.com.
Sci Rep ; 14(1): 11678, 2024 05 22.
Article en En | MEDLINE | ID: mdl-38778219
ABSTRACT
Polyps are abnormal tissue clumps growing primarily on the inner linings of the gastrointestinal tract. While such clumps are generally harmless, they can potentially evolve into pathological tumors, and thus require long-term observation and monitoring. Polyp segmentation in gastrointestinal endoscopy images is an important stage for polyp monitoring and subsequent treatment. However, this segmentation task faces multiple challenges the low contrast of the polyp boundaries, the varied polyp appearance, and the co-occurrence of multiple polyps. So, in this paper, an implicit edge-guided cross-layer fusion network (IECFNet) is proposed for polyp segmentation. The codec pair is used to generate an initial saliency map, the implicit edge-enhanced context attention module aggregates the feature graph output from the encoding and decoding to generate the rough prediction, and the multi-scale feature reasoning module is used to generate final predictions. Polyp segmentation experiments have been conducted on five popular polyp image datasets (Kvasir, CVC-ClinicDB, ETIS, CVC-ColonDB, and CVC-300), and the experimental results show that the proposed method significantly outperforms a conventional method, especially with an accuracy margin of 7.9% on the ETIS dataset.
Asunto(s)
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Pólipos del Colon Límite: Humans Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Pólipos del Colon Límite: Humans Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido