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PKSEA-Net: A prior knowledge supervised edge-aware multi-task network for retinal arteriolar morphometry.
Huang, Chongjun; Wang, Zhuoran; Yuan, Guohui; Xiong, Zhiming; Hu, Jing; Tong, Yuhua.
Afiliación
  • Huang C; School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang 324000, China.
  • Wang Z; School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang 324000, China.
  • Yuan G; School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang 324000, China.
  • Xiong Z; School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang 324000, China.
  • Hu J; School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang 324000, China.
  • Tong Y; The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, Zhejiang 324000, China. Electronic address: yuhuatong@126.com.
Comput Biol Med ; 172: 108255, 2024 Apr.
Article en En | MEDLINE | ID: mdl-38461696
ABSTRACT
Retinal fundus images serve as a non-invasive modality to obtain information pertaining to retinal vessels through fundus photography, thereby offering insights into cardiovascular and cerebrovascular diseases. Retinal arteriolar morphometry has emerged as the most convenient and fundamental clinical methodology in the realm of patient screening and diagnosis. Nevertheless, the analysis of retinal arterioles is challenging attributable to imaging noise, stochastic fuzzy characteristics, and blurred boundaries proximal to blood vessels. In response to these limitations, we introduce an innovative methodology, named PKSEA-Net, which aims to improve segmentation accuracy by enhancing the perception of edge information in retinal fundus images. PKSEA-Net employs the universal architecture PVT-v2 as the encoder, complemented by a novel decoder architecture consisting of an Edge-Aware Block (EAB) and a Pyramid Feature Fusion Module (PFFM). The EAB block incorporates prior knowledge for supervision and multi-query for multi-task learning, with supervision information derived from an enhanced Full Width at Half Maximum (FWHM) algorithm and gradient map. Moreover, PFFM efficiently integrates multi-scale features through a novel attention fusion method. Additionally, we have collected a Retinal Cross-Sectional Vessel (RCSV) dataset derived from approximately 200 patients in Quzhou People's Hospital to serve as the benchmark dataset. Comparative evaluations with several state-of-the-art (SOTA) networks confirm that PKSEA-Net achieves exceptional experimental performance, thereby establishing its status as a SOTA approach for precise boundary delineation and retinal vessel segmentation.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Vasos Retinianos / Aprendizaje Límite: Humans Idioma: En Revista: Comput Biol Med Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Vasos Retinianos / Aprendizaje Límite: Humans Idioma: En Revista: Comput Biol Med Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos