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

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.