Attention-guided channel to pixel convolution network for retinal layer segmentation with choroidal neovascularization

Xiaoling Yang; Xinjian Chen; Dehui Xiang

doi:10.1117/12.2548940

10 March 2020 Attention-guided channel to pixel convolution network for retinal layer segmentation with choroidal neovascularization

Xiaoling Yang, Xinjian Chen, Dehui Xiang

Author Affiliations +

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 1131334 (2020) https://doi.org/10.1117/12.2548940
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

This paper introduces an attention-guided channel to pixel convolution network for a fully automatic segmentation of retinal layers with choroidal neovascularization from optical coherence tomography (OCT) images. The proposed framework, consists of two new strategies for retinal layers segmentation: Channel to Pixel Block and Attention block. To deal with the contrast reduction of adjacent retinal layers caused by choroidal neovascularization, we firstly design a Channel to Pixel Block to convert particular channels into pixels in one bigger feature map, followed by a convolution layer optimized by a novel edge loss. Faced with large morphological changes of retinal layers, the attention mechanism is then introduced to extract more context information. The proposed method was trained on augmented 1280 OCT images and tested on 384 OCT images with choroidal neovascularization. The experimental results showed that the proposed method outperformed the state-of-art methods for retinal OCT image segmentation.

Citation Download Citation

Xiaoling Yang, Xinjian Chen, and Dehui Xiang "Attention-guided channel to pixel convolution network for retinal layer segmentation with choroidal neovascularization", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 1131334 (10 March 2020); https://doi.org/10.1117/12.2548940

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