Attention multi-scale network for pigment epithelial detachment segmentation in OCT images

Dengsen Bao; Xuena Cheng; Weifang Zhu; Fei Shi; Xinjian Chen

doi:10.1117/12.2548959

10 March 2020 Attention multi-scale network for pigment epithelial detachment segmentation in OCT images

Dengsen Bao, Xuena Cheng, Weifang Zhu, Fei Shi, Xinjian Chen

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 1131335 (2020) https://doi.org/10.1117/12.2548959
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Accurate segmentation of pigment epithelial detachment (PED) in retinal optical coherence tomography (OCT) images can help doctors comprehensively analyze and diagnose chorioretinal diseases, such as age-related macular degeneration (AMD), central serous chorioretinopathy and polypoidal choroidal vasculopathy. Due to the serious uneven sizes of PED, some traditional algorithms or common deep networks do not perform well in PED segmentation. In this paper, we propose a novel attention multi-scale network (named as AM-Net) based on a U-shape network to segment PED in OCT images. Compared with the original U-Net, there are two main improvements in the proposed method: (1) Designing channel multiscale module (CMM) to replace the skip-connection layer of the U-Net, which uses channel attention mechanism to obtain multi-scale information. (2) Designing spatial multi-scale module (SMM) based on dilated convolution, which is inserted in the decoder path to make the network pay more attention on the multi-scale spatial information. We evaluated the proposed AM-Net on 240 clinically obtained OCT B-scans with 4-fold cross validation. The mean and standard deviation of Intersection over Union (IoU), Dice Similarity Coefficient (DSC), Sensitivity (Sen) and Specificity (Spe) are 72.12± 9.60%, 79.17±8.25%, 93.05±1.72% and 79.93±5.77%, respectively.

Citation Download Citation

Dengsen Bao, Xuena Cheng, Weifang Zhu, Fei Shi, and Xinjian Chen "Attention multi-scale network for pigment epithelial detachment segmentation in OCT images", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 1131335 (10 March 2020); https://doi.org/10.1117/12.2548959

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