Fully automated segmentation of hyper-reflective foci in OCT images using a U-shape network

Liangjiu Zhu; Weifang Zhu; Shuanglang Feng; Xinjian Chen

doi:10.1117/12.2548085

10 March 2020 Fully automated segmentation of hyper-reflective foci in OCT images using a U-shape network

Liangjiu Zhu, Weifang Zhu, Shuanglang Feng, Xinjian Chen

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 1131308 (2020) https://doi.org/10.1117/12.2548085
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Diabetic retinopathy (DR), a highly specific vascular complication caused by diabetes, has been found a major cause of blindness in the world. Early screening of DR is crucial for prevention of vision loss. Hard exudates (HEs) is one of the main manifestations of DR, which is characterized by hyper-reflective foci (HF) in retinal optical coherence tomography(OCT) images. In this paper, a fully automated method based on U-shape network is proposed to segment HF in retinal OCT images. Compared with the original U-Net, there are two main improvements in the proposed network:(1) The ordinary 3×3 convolution is replaced by multi-scale convolution based on dilated convolution, which can achieve adaptive receptive fields of the images. (2) In order to ignore irrelevant information and focus on key information in the channels, the channel attention module is embedded in the model. A dataset consisting of 112 2D OCT B-scan images was used to evaluate the proposed U-shape network for HF segmentation with 4-fold cross validation. The mean and standard deviation of Dice similarity coefficient, recall and precision are 73.26±2.03%, 75.71±1.98% and 74.28± 2.67%, respectively. The experimental results show the effectiveness of the proposed method.

Conference Presentation

Citation Download Citation

Liangjiu Zhu, Weifang Zhu, Shuanglang Feng, and Xinjian Chen "Fully automated segmentation of hyper-reflective foci in OCT images using a U-shape network", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 1131308 (10 March 2020); https://doi.org/10.1117/12.2548085

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