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Proceedings Paper

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

Paper Details

Date Published: 10 March 2020
PDF: 7 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 1131334 (10 March 2020); doi: 10.1117/12.2548940
Show Author Affiliations
Xiaoling Yang, Soochow Univ. (China)
Xinjian Chen, Soochow Univ. (China)
Dehui Xiang, Soochow Univ. (China)


Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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