SCVi-Net：一种基于混合模型的视网膜血管分割方法

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中图分类号：TP391 文献标志码：A

Abstract： The existing retinal vascular segmentation methods are often limited to the local receptive field. It is difficult to effectively capture the global information，and the performance of vascular structures at different scales varies greatly， which makes it difficult to process features at different scales at the same time. In order to address the problems， an efcient retinal vascular segmentation model SCVi-Net is proposed. Based on U-Net， the model improves the hop connection， introduces a new SCA module， and enhances the feature extraction ability by adaptively adjusting the space and channel weights. By adding a ViT module to the deepest layer of the encoder， SCVi-Net improves the ability to capture global information. The ASPP module effectively extracts multi-scale features and enhances the robustness of the network. The SSF module optimizes the training process by fusing multiple side outputs and improves the segmentation accuracy of the vascular region. In order to evaluate the superiority of the model， comparative experiments are conducted on DRIVE， CHASEDB1 and STARE datasets. The results show that SCVi-Net has high segmentation accuracy and robustness in complex retinal vascular images.

Keywords： retinal vascular segmentation; U-Net; joint atention mechanism; transformer; atrous spatial pyramid pooling; SCVi-Net; medical image processing

视网膜血管结构分析在眼科疾病的筛查和诊断中具有重要意义，尤其对糖尿病视网膜病变和脉络膜新生血管等血管性眼病的评估至关重要[1-3]。（剩余17535字）