Deep Learning with Skip Connection Attention for Choroid Layer Segmentation in OCT Images

Since the thickness and shape of the choroid layer are indicators for the diagnosis of several ophthalmic diseases, the choroid layer segmentation is an important task. There exist many challenges in segmentation of the choroid layer. In this paper, in view of the lack of context information due to the ambiguous boundaries, and the subsequent inconsistent predictions of the same category targets ascribed to the lack of context information or the large regions, a novel Skip Connection Attention (SCA) module which is integrated into the U-Shape architecture is proposed to improve the precision of choroid layer segmentation in Optical Coherence Tomography (OCT) images. The main function of the SCA module is to capture the global context in the highest level to provide the decoder with stage-by-stage guidance, to extract more context information and generate more consistent predictions for the same class targets. By integrating the SCA module into the U-Net and CE-Net, we show that the module improves the accuracy of the choroid layer segmentation.