A Lightweight Depthwise Separable ConvNet with Frequency-domain Enhancement for Retinal Vessel Segmentation

Automatic retinal vessel segmentation is crucial in the diagnosis and treatment of various cardiovascular and eye diseases. Although current vessel segmentation methods have achieved impressive performance, some challenging issues still need to be addressed. For example, existing methods always cannot segment complex capillaries well because they may be interfered with or covered by other components in the retina, and they need to further improve the continuity and consistency of vessel segmentation results. Moreover, the excellent vessel segmentation methods are usually built on bulky and cumbersome models which greatly limit their application range. In this article, we propose a novel efficient depthwise separable convolution network with frequency-domain enhancement (dubbed RetiNeXt) for retinal vessel segmentation. Firstly, we design a lightweight vessel enhancement module to extract global fine topological structure features from the frequency domain to enhance the complex capillary vessel details. Secondly, we propose a global feature extraction block to fully capture the large-scale spatial information and global characterizations, which enables the model to maintain vessel structural coherence from a global perspective. Thirdly, we construct a local feature mixing block based on SimAM attention mechanism to highlight the tiny capillary topological structure features and optimize the segmentation of low-contrast blood vessels, thereby improving the integrity and continuity of complex capillaries. Comprehensive comparison experiments on three well-benchmarked retinal vessel segmentation datasets fully verify the effectiveness and superiority of the proposed RetiNeXt. To further demonstrate the universality of RetiNeXt for medical image segmentation, we also conduct sufficient comparative experiments on two classical coronary angiography datasets. Extensive quantitative and qualitative experiments fully show that RetiNeXt outperforms other state-of-the-art methods with only 0.4M of trainable parameters.