Retinal Vessel Segmentation by Deep Residual Learning with Wide Activation

Purpose. Retinal blood vessel image segmentation is an important step in ophthalmological analysis. However, it is difficult to segment small vessels accurately because of low contrast and complex feature information of blood vessels. The objective of this study is to develop an improved retinal blo...

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Bibliographic Details
Published in:Computational intelligence and neuroscience 2020, Vol.2020 (2020), p.1-11
Main Authors: Peng, Yun, Duan, Xiaopeng, Li, Xue, Ma, Yuliang, Zhang, Yingchun
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Balances (scales)
Blood vessels
Complexity
Datasets
Deep learning
Diabetic retinopathy
Goals
Image processing
Image Processing, Computer-Assisted
Image segmentation
Medical research
Methods
Morphology
Neural networks
Retina
Retinal Vessels - diagnostic imaging
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Summary:Purpose. Retinal blood vessel image segmentation is an important step in ophthalmological analysis. However, it is difficult to segment small vessels accurately because of low contrast and complex feature information of blood vessels. The objective of this study is to develop an improved retinal blood vessel segmentation structure (WA-Net) to overcome these challenges. Methods. This paper mainly focuses on the width of deep learning. The channels of the ResNet block were broadened to propagate more low-level features, and the identity mapping pathway was slimmed to maintain parameter complexity. A residual atrous spatial pyramid module was used to capture the retinal vessels at various scales. We applied weight normalization to eliminate the impacts of the mini-batch and improve segmentation accuracy. The experiments were performed on the DRIVE and STARE datasets. To show the generalizability of WA-Net, we performed cross-training between datasets. Results. The global accuracy and specificity within datasets were 95.66% and 96.45% and 98.13% and 98.71%, respectively. The accuracy and area under the curve of the interdataset diverged only by 1%∼2% compared with the performance of the corresponding intradataset. Conclusion. All the results show that WA-Net extracts more detailed blood vessels and shows superior performance on retinal blood vessel segmentation tasks.
ISSN:1687-5265
1687-5273
DOI:10.1155/2020/8822407