摘要
Retinal images play an essential role in the early diagnosis of ophthalmic diseases.Automatic segmentation of retinal vessels in color fundus images is challenging due to the morphological differences between the retinal vessels and the low-contrast background.At the same time,automated models struggle to capture representative and discriminative retinal vascular features.To fully utilize the structural information of the retinal blood vessels,we propose a novel deep learning network called Pre-Activated Convolution Residual and Triple Attention Mechanism Network(PCRTAM-Net).PCRTAM-Net uses the pre-activated dropout convolution residual method to improve the feature learning ability of the network.In addition,the residual atrous convolution spatial pyramid is integrated into both ends of the network encoder to extract multiscale information and improve blood vessel information flow.A triple attention mechanism is proposed to extract the structural information between vessel contexts and to learn long-range feature dependencies.We evaluate the proposed PCRTAM-Net on four publicly available datasets,DRIVE,CHASE_DB1,STARE,and HRF.Our model achieves state-of-the-art performance of 97.10%,97.70%,97.68%,and 97.14%for ACC and 83.05%,82.26%,84.64%,and 81.16%for F1,respectively.
作者
汪华登
李紫正
保罗
黎兵兵
潘细朋
刘振丙
蓝如师
罗笑南
Hua-Deng Wang;Zi-Zheng Li;Idowu Paul Okuwobi;Bing-Bing Li;Xi-Peng Pan;Zhen-Bing Liu;Ru-Shi Lan;Xiao-Nan Luo(Guangxi Key Laboratory of Image and Graphic Intelligent Processing,Guilin 541004,China;School of Computer Science and Information Security,Guilin University of Electronic Technology,Guilin 541004,China;School of Artificial Intelligence,Guilin University of Electronic Technology,Guilin 541004,China;Department of Pathology,Ganzhou Municipal Hospital,Ganzhou 341000,China)
基金
supported by the Open Funds from Guangxi Key Laboratory of Image and Graphic Intelligent Processing under Grant No.GIIP2209
the National Natural Science Foundation of China under Grant Nos.62172120 and 62002082
the Natural Science Foundation of Guangxi Province of China under Grant Nos.2019GXNSFAA245014 and 2020GXNSFBA238014.
