光学学报, 2019, 39 (2): 0211002, 网络出版: 2019-05-10   

基于全卷积神经网络的多尺度视网膜血管分割 下载: 1827次

Multi-Scale Retinal Vessel Segmentation Based on Fully Convolutional Neural Network
作者单位
1 天津大学电气自动化与信息工程学院, 天津 300072
2 大连理工大学工业装备结构分析国家重点实验室, 辽宁 大连 116024
摘要
提出了一种基于多尺度特征融合的全卷积神经网络的视网膜血管分割方法,无需手工设计特征和后处理过程。利用跳跃连接构建编码器-解码器结构全卷积神经网络,将高层语义信息和低层特征信息进行融合;利用残差块进一步学习细节和纹理特征;利用不同空洞率的空洞卷积构建多尺度空间金字塔池化结构,进一步扩大感受野,充分结合图像上下文信息;采用类别平衡损失函数解决正负样本不均衡问题。实验结果表明,在DRIVE(Digital Retinal Images for Vessel Extraction)和STARE (Structured Analysis of the Retina)数据集上的准确率分别为95.46%和96.84%,敏感性分别为80.53%和82.99%,特异性分别为97.67%和97.94%,受试者工作特征(ROC)曲线下的面积分别为97.71%和98.17%。所提方法相较于其他方法性能更优。
Abstract
A method for retinal vessel segmentation is proposed based on a fully convolutional neural network with multi-scale feature fusion, which does not need hand-crafted features or specific post-processing. The architecture of skip connection is utilized, which combines the high-level semantic information with the low-level features. Residual block has been introduced to help learn details and texture features. The multi-scale spatial pyramid pooling module is built by atrous convolutions with different atrous rates to further enlarge the receptive fields and fully combine the context information. The class-balanced loss function is applied to solve the problem of imbalanced distribution of samples. The experimental results show that in the two datasets of digital retinal images for vessel extraction (DRIVE) and structured analysis of the retina (STARE), the accuracies are 95.46% and 96.84%, the sensitivities are 80.53% and 82.99%, the specificities are 97.67% and 97.94%, and the areas under receiver operating characteristic (ROC) curve are 97.71% and 98.17%, respectively. The proposed method is superior to the other existing methods.

郑婷月, 唐晨, 雷振坤. 基于全卷积神经网络的多尺度视网膜血管分割[J]. 光学学报, 2019, 39(2): 0211002. Tingyue Zheng, Chen Tang, Zhenkun Lei. Multi-Scale Retinal Vessel Segmentation Based on Fully Convolutional Neural Network[J]. Acta Optica Sinica, 2019, 39(2): 0211002.

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