分割
人工智能
计算机科学
模式识别(心理学)
光学相干层析成像
编码器
棱锥(几何)
联营
图像分割
尺度空间分割
计算机视觉
数学
医学
眼科
几何学
操作系统
作者
Jiadong Yang,Yu-Hui Tao,Qiuzuo Xu,Yuhan Zhang,Xiao Ma,Songtao Yuan,Qiang Chen
出处
期刊:IEEE Journal of Biomedical and Health Informatics
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:26 (8): 3872-3883
被引量:2
标识
DOI:10.1109/jbhi.2022.3166778
摘要
Automated layer segmentation plays an important role for retinal disease diagnosis in optical coherence tomography (OCT) images. However, the severe retinal diseases result in the performance degeneration of automated layer segmentation approaches. In this paper, we present a robust semi-supervised layer segmentation network to relieve the model failures on abnormal retinas. We obtain the lesion features from the labeled images with disease-balanced distribution, and utilize the unlabeled images to supplement the layer structure information. Specifically, in our method, the cross-consistency training is utilized over the predictions of different decoders, and we enforce a consistency between different decoder predictions to improve the encoder's representation. Then, we propose a sequence prediction branch based on self-supervised manner, which is designed to predict the position of each jigsaw puzzle to obtain sensory perception of the retinal layer structure. To this task, a layer spatial pyramid pooling (LSPP) module is designed to extract multi-scale layer spatial features. Furthermore, we use the optical coherence tomography angiography (OCTA) to supplement the information damaged by diseases. The experimental results illustrate that our method achieves more robust results compared with current supervised segmentation methods. Meanwhile, advanced segmentation performance can be obtained compared with state-of-the-art semi-supervised segmentation methods.
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