Automatic Retinal Blood Vessel Segmentation Based on Fully Convolutional Neural Networks

被引:54
|
作者
Jiang, Yun [1 ]
Zhang, Hai [1 ]
Tan, Ning [1 ]
Chen, Li [1 ]
机构
[1] Northwest Normal Univ, Coll Comp Sci & Engn, Lanzhou 730070, Peoples R China
来源
SYMMETRY-BASEL | 2019年 / 11卷 / 09期
基金
中国国家自然科学基金;
关键词
retinal image; vessel segmentation; fully convolutional neural network; IMAGES; TRACKING;
D O I
10.3390/sym11091112
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Automated retinal vessel segmentation technology has become an important tool for disease screening and diagnosis in clinical medicine. However, most of the available methods of retinal vessel segmentation still have problems such as poor accuracy and low generalization ability. This is because the symmetrical and asymmetrical patterns between blood vessels are complicated, and the contrast between the vessel and the background is relatively low due to illumination and pathology. Robust vessel segmentation of the retinal image is essential for improving the diagnosis of diseases such as vein occlusions and diabetic retinopathy. Automated retinal vein segmentation remains a challenging task. In this paper, we proposed an automatic retinal vessel segmentation framework using deep fully convolutional neural networks (FCN), which integrate novel methods of data preprocessing, data augmentation, and full convolutional neural networks. It is an end-to-end framework that automatically and efficiently performs retinal vessel segmentation. The framework was evaluated on three publicly available standard datasets, achieving F1 score of 0.8321, 0.8531, and 0.8243, an average accuracy of 0.9706, 0.9777, and 0.9773, and average area under the Receiver Operating Characteristic (ROC) curve of 0.9880, 0.9923 and 0.9917 on the DRIVE, STARE, and CHASE_DB1 datasets, respectively. The experimental results show that our proposed framework achieves state-of-the-art vessel segmentation performance in all three benchmark tests.
引用
收藏
页数:22
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