采用双支路和Transformer的视杯视盘分割方法.

The complexity of the retinal vasculature and similar background to the optic cup optic disc region are the reasons for the poor accuracy of the optic cup and optic disc segmentation. We design a segmentation model with dual-branch feature fusion to improve the segmentation performance. The main branch of the model uses a Transformer for feature extraction, which makes up for the deficiency of convolutional operations in establishing remote relationships. We use several modules to fuse shallow spatial features with high-level semantic features: SCA-FFM (Scale Awareness-Feature Fusion Module) is used to collect semantic and positional information about the optic disc and optic cup from high-level features; IM (Identification Module) uses an attention mechanism to reduce the error information and noise present in low-level features and enhance the extraction of spatial detail features; GCD-FFM (Graph Convolution Domain- feature fusion module) to fuse high-level semantic features and low-level features so that the feature map has both global and local information. The comparison experiments show that the method in this paper exhibits a better segmentation effect and good generalization ability. [ABSTRACT FROM AUTHOR]