Context attention-and-fusion network for multiclass retinal fluid segmentation in OCT images

Abstract

Optical coherence tomography (OCT) is an imaging modality that is extensively used for ophthalmic diagnosis and treatment. OCT can help reveal disease-related alterations below the surface of the retina, such as retinal fluid which can cause vision impairment. In this paper, we propose a novel context attention-and-fusion network (named as CAF-Net) for multiclass retinal fluid segmentation, including intraretinal fluid (IRF), subretinal fluid (SRF) and pigment epithelial detachment (PED). To deal with the seriously uneven sizes and irregular distributions of different types of fluid, our CAF-Net proposes the context shrinkage encode (CSE) module and context pyramid guide (CPG) module to extract and fuse global context information. The CSE module embedded in the encoder path can ignore redundant information and focus on useful information by a shrinkage function. Besides, the CPG module is inserted between the encoder and decoder, which can dynamically fuse multi-scale information in high-level features. The proposed CAF-Net was evaluated on a public dataset from RETOUCH Challenge in MICCAI2017, which consists of 70 OCT volumes with three types of retinal fluid from three different types of devices. The average of Dice similarity coefficient (DSC) and Intersection over Union (IoU) are 74.64% and 62.08%, respectively.