An automatic retinal vessel segmentation approach based on Convolutional Neural Networks

2021 ◽  
pp. 115459
Author(s):  
Chala Mohamed ◽  
Benayad Nsiri ◽  
My Hachem El yousfi Alaoui ◽  
Soulaymani Abdelmajid ◽  
Mokhtari Abdelghani ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Segmentation Approach ◽  
Retinal Vessel Segmentation

scholarly journals Automatic Retinal Blood Vessel Segmentation Based on Fully Convolutional Neural Networks

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1112 ◽  
Author(s):  
Yun Jiang ◽  
Hai Zhang ◽  
Ning Tan ◽  
Li Chen
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Clinical Medicine ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Benchmark Tests ◽  
Fully Convolutional Neural Networks ◽  
Average Accuracy ◽  
Retinal Vessel Segmentation

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.

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