数字中医药（英文）2022，Vol.5Issue(4)：406-418,13.DOI:10.1016/j.dcmed.2022.12.008
MF2ResU-Net:一种面向视网膜血管分割的多特征融合深度网络构架
MF2ResU-Net: a multi-feature fusion deep learning architecture for retinal blood vessel segmentation
摘要
Abstract
Objective For computer-aided Chinese medical diagnosis and aiming at the problem of in-sufficient segmentation, a novel multi-level method based on the multi-scale fusion residual neural network (MF2ResU-Net) model is proposed.Methods To obtain refined features of retinal blood vessels, three cascade connected U-Net networks are employed. To deal with the problem of difference between the parts of en-coder and decoder, in MF2ResU-Net, shortcut connections are used to combine the encoder and decoder layers in the blocks. To refine the feature of segmentation, atrous spatial pyram-id pooling (ASPP) is embedded to achieve multi-scale features for the final segmentation net-works. Results The MF2ResU-Net was superior to the existing methods on the criteria of sensitivity (Sen), specificity (Spe), accuracy (ACC), and area under curve (AUC), the values of which are 0.8013 and 0.8102, 0.9842 and 0.9809, 0.9700 and 0.9776, and 0.9797 and 0.9837, respectively for DRIVE and CHASE DB1. The results of experiments demonstrated the effectiveness and robustness of the model in the segmentation of complex curvature and small blood vessels. Conclusion Based on residual connections and multi-feature fusion, the proposed method can obtain accurate segmentation of retinal blood vessels by refining the segmentation fea-tures, which can provide another diagnosis method for computer-aided Chinese medical dia-gnosis.关键词
医学图像处理/空洞空间金字塔池化/冗余连接/多级模型/视网膜血管分割Key words
Medical image processing/Atrous space pyramid pooling (ASPP)/Residual neural network/Multi-level model/Retinal vessels segmentation引用本文复制引用
崔振超,宋姝洁,齐静..MF2ResU-Net:一种面向视网膜血管分割的多特征融合深度网络构架[J].数字中医药（英文）,2022,5(4):406-418,13.基金项目
Key R&D Projects in Hebei Province(22370301D),Sci-entific Research Foundation of Hebei University for Distinguished Young Scholars(521100221081),and  （22370301D）
Scientific Research Foundation of Colleges and Universit-ies in Hebei Province(QN2022107). （QN2022107）
