Multie-scale hierarchical feature extraction combined with dilated-residual U-Net for retina automatic segmentation
Journal Title: Eye Science (Yanke Xuebao) - Year 2021, Vol 36, Issue 1
Abstract
[Objective:] To achieve the segmentation of different layers and fluid areas on the optical coherence tomography (OCT) image of the retina. [Methods:] A lightweight neural network based on deep learning was proposed. The network structure adopted in this study was designed based on the architecture of dilated-residual U-Net. By connecting the upsampling output obtained at different depth networks, multi-scale feature fusion was performed to enable the system to accurately identify the boundaries on the OCT image. [Results:] Compared with U-Net, this algorithm could achieve the same accuracy with 1–2 epochs less, and the accuracy was also improved by 1.25%. [Conclusion:] The proposed network improves the segmentation performance of retinal OCT images, and reduces the number of parameters, which demonstrates the network has great application potential.
Authors and Affiliations
Shanshan LIANG, Hongwei ZENG, Jie HE, Jun ZHANG, Jin YUAN
Keywords
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- EP ID EP763458
- DOI 10.3978/j.issn.1000-4432.2021.01.10
- Views 25
- Downloads 0
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