融合残差注意密集双u网络视网膜血管分割算法

Chunhui Zhu, Xiaowei Niu, Lu Zuo, Ziwei Liu
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引用次数: 0

摘要

针对现有视网膜血管整体分割算法准确率略低的问题,提出了一种融合残差注意密集卷积的双u网络LSCD-UNet(基于scse -残差、CBAM和密集腔卷积的Laddernet网络)。梯子网,UNet的一种形式,被引入到网络中。在此基础上,将共享权值的残差模块升级为共享权值的scse -残差模块,便于特征增强提取。在该网络的底部引入了一个多模块,由卷积注意机制模块(CBAM)和密集腔卷积模块(DAC)串联组成,以扩大视野并捕捉更细微的血管特征。采用混合损失函数加快网络收敛速度。LSCD-UNet算法在DRIVE和STARE两个公共数据集上进行了验证。结果表明,LSCD-UNet算法的准确率分别为97.35%和97.28%,灵敏度分别为81.80%和86.23%,AUC分别为98.82%和99.02%,F1值分别为84.89%和84.97%,优于UNet和Laddernet等视网膜血管分割算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fused Residual Attention Dense Double-U Network Retinal Vessel Segmentation Algorithm
In view of the slightly low accuracy of the existing overall segmentation algorithm of retinal vessels, a fused residual attention dense convolution double-U network, LSCD-UNet (Laddernet network based on scSE-Residual and CBAM and dense cavity convolution) was proposed.. Laddernet, a form of UNet, was introduced into the network. On this basis, the residual module with shared weights was upgraded and replaced with the scSE-Residual module of shared weights to facilitate feature enhancement extraction. A multi-module was introduced at the bottom of this network, consisting of the Convolutional Attention Mechanism Module (CBAM) and Dense Cavity Convolution Module (DAC) in series to expand the field of view and capture more subtle vascular features. Hybrid loss function was used to accelerate the network convergence. The LSCD-UNet algorithm was validated on the public datasets, DRIVE and STARE,. The results showed that the LSCD-UNet algorithm had an accuracy of 97.35% and 97.28%, a sensitivity of 81.80% and 86.23%, an AUC of 98.82% and 99.02%, and an F1 value of 84.89% and 84.97%, respectively, outperforming UNet and Laddernet and other retinal vessel segmentation algorithms.
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