MLU-Net: Efficient Segmentation for Retinal Layers In Optical Coherence Tomography Images. Issue 1 (June 2021)

• Record Type: Journal Article
• Authors: Xu, Xiangcong; Wang, Xuehua; Han, Dingan; Guo, Xuedong; Lin, Jingyi; Xiong, Ke; Zheng, Yixu; Zeng, Yaguang
• Is Part Of: Journal of physics. Volume 1955:Issue 1(2021)
• Publication Date: 2021-06
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1955/1/012053
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: The automatic segmentation of retinal images obtained by optical coherence tomography is increasingly important for ophthalmologists to diagnose and monitor many kinds of ophthalmic diseases. U-Net is the most widely used deep learning network in retinal segmentation, but the limited number of data-flow paths made it hard to capture complex features. We proposed here an optimized Mobile Ladder U-Net (MLU-Net), which consists of a Ladder Connection for increasing the network's data-flow paths and a depthwise separable convolution for reducing the model's parameters. Experiments on 100 B-scans from 10 human eyes demonstrated that the 9 retinal layer boundaries can be segmented accurately with the MLU-Net. In addition, compared with the original U-Net and LadderNet, the segmentation result of our method is closest to the expert label.