CSGNet: Cascade semantic guided net for retinal vessel segmentation

Abstract

Retinal vessels are essential biomarkers for the early diagnosis of ophthalmic diseases. Accurate segmentation of retinal vessels is necessary for further quantitative analysis of fundus images. With the development of deep learning, many deep segmentation models have been proposed and show promising results. Nevertheless, segmentation performance is not satisfactory in tiny vessels and low-contrast vessels. A cascade semantic guided network (CSGNet) is proposed to address this problem. CSGNet follows a low-to-high pipeline, where low-resolution semantic-rich features are first learned. Then, higher-resolution features are learned under the guidance of low-resolution features. In this way, CSGNet is expected to learn both semantic-rich and detail-preserved high-resolution representations to facilitate the segmentation of thin vessels. Meantime, a multi-scale and multi-directional feature learning (M2FL) module is developed, which leverages two strip convolutions and two dilated convolutions to align with the shape of vessels and encode context information. Extensive experiments are conducted to validate the segmentation performance of the proposed method over six public datasets, including four photography datasets (CHASE_DB1, DRIVE, STARE, HRF) and two color scanning laser ophthalmoscopy datasets (IOSTAR, RC-SLO). Also, the proposed method is compared with several popular deep learning-based models. Experimental results show that the proposed method achieves comparable or superior segmentation performance compared with other deep learning-based models with fewer parameters and faster segmentation speed. Meantime, cross-training experiments demonstrate the competitive generalization capability of the proposed method.