Graph-based convolution feature aggregation for retinal vessel segmentation

Abstract

Retinal vessel segmentation is a crucial task in computer assistant diagnosis of eye diseases. Instead of relying heavily on crafted features, high dimensional deep learning convolution features has provided better representation. However, the neglecting of inherent relation among multiple features is problematic. To explore non-local contextual dependencies, we proposed a graph-based convolution feature aggregation network (GCFAN) for segmenting retinal vessel and enhancing image non-vessel region simultaneously relying on graph to propagate and aggregate message of cross-level features. Specifically, the model consists of three modules: multi-level feature extraction module (MFEM), graph-based high level convolution feature aggregation module (GHFAM), graph-based low level convolution feature aggregation module (GLFAM). The multi-level feature representations are extracted from retinal image in MFEM. GHFAM utilizes more semantic information to reconstruct retinal image without vessel, which facilitates diagnosis. GLFAM utilizes more boundary information to segment vessel. Competitive experimental results on four retinal image datasets validate the efficacy of the proposed model, which achieves segmentation and reconstruct retinal image without vessel, indicating its potential clinical application. Finally, an IoT framework which integrates our algorithm is built to analyze image from various fundus cameras in different places and display results on PC and mobile phone simultaneously, which will facilitate doctor diagnose.