Retinal Vessel Segmentation by Deep Residual Learning with Wide Activation
Joint Authors
Li, Xue
Duan, Xiaopeng
Peng, Yun
Ma, Yuliang
Zhang, Yingchun
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-10
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Ma, Yuliang& Li, Xue& Duan, Xiaopeng& Peng, Yun& Zhang, Yingchun. 2020. Retinal Vessel Segmentation by Deep Residual Learning with Wide Activation. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1138856
Modern Language Association (MLA)
Ma, Yuliang…[et al.]. Retinal Vessel Segmentation by Deep Residual Learning with Wide Activation. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1138856
American Medical Association (AMA)
Ma, Yuliang& Li, Xue& Duan, Xiaopeng& Peng, Yun& Zhang, Yingchun. Retinal Vessel Segmentation by Deep Residual Learning with Wide Activation. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1138856
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138856