DEF-Net:用于眼底视网膜血管分割的双编码器融合网络

IF 2.6 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Jianyong Li, Ge Gao, Lei Yang, Yanhong Liu, Hongnian Yu
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引用次数: 3

摘要

许多眼病的恶化与眼底视网膜结构密切相关,因此视网膜血管自动分割是临床上有效检测眼相关病变的重要环节。基于编码-解码结构的分割方法在视网膜血管分割任务中表现出很大的潜力,但其特征表示能力有限。此外,它们在进行特征融合时没有有效地考虑多尺度信息,导致融合效率较低。本文设计了一种新的视网膜血管自动分割模型DEF-Net,该模型由一个双编码器单元和一个解码器单元组成。将递归网络和卷积网络融合,提出了一种双编码器单元,构建卷积网络分支提取细节特征,构建递归网络分支积累上下文特征,与单一卷积网络结构相比,可以获得更丰富的特征。此外,为了挖掘多尺度的有用信息,设计了多尺度融合块,提高了特征融合效率。已经进行了大量的实验来证明我们提出的DEF-Net的分割性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DEF-Net: A Dual-Encoder Fusion Network for Fundus Retinal Vessel Segmentation
The deterioration of numerous eye diseases is highly related to the fundus retinal structures, so the automatic retinal vessel segmentation serves as an essential stage for efficient detection of eye-related lesions in clinical practice. Segmentation methods based on encode-decode structures exhibit great potential in retinal vessel segmentation tasks, but have limited feature representation ability. In addition, they don’t effectively consider the information at multiple scales when performing feature fusion, resulting in low fusion efficiency. In this paper, a newly model, named DEF-Net, is designed to segment retinal vessels automatically, which consists of a dual-encoder unit and a decoder unit. Fused with recurrent network and convolution network, a dual-encoder unit is proposed, which builds a convolutional network branch to extract detailed features and a recurrent network branch to accumulate contextual features, and it could obtain richer features compared to the single convolution network structure. Furthermore, to exploit the useful information at multiple scales, a multi-scale fusion block used for facilitating feature fusion efficiency is designed. Extensive experiments have been undertaken to demonstrate the segmentation performance of our proposed DEF-Net.
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来源期刊
Electronics
Electronics Computer Science-Computer Networks and Communications
CiteScore
1.10
自引率
10.30%
发文量
3515
审稿时长
16.71 days
期刊介绍: Electronics (ISSN 2079-9292; CODEN: ELECGJ) is an international, open access journal on the science of electronics and its applications published quarterly online by MDPI.
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