LAUSR

Accurate segmentation of retinal blood vessels is an important prerequisite for diagnosing many fundus diseases. Many methods based on artificially designed convolutional neural networks (CNNs) have been successively proposed and achieved good results. However, the image quality collected by fundus camera is poor, and the vessels topology in the image is complex, the pure convolution feature extraction in these CNN methods is limited. A novel multilevel remote relational modeling network (MRRM-Net) is proposed, which can balance global/local long-range contextual relations through building multidimensional and multilevel attention. Based on the symmetric structure, we first design the three-dimensional guided attention module (TGAM) in the encoder to establish long-range dependency descriptions with semantically rich spatial structure through similarity and then design a cross-level sensitive attention module (CSAM) at the skip connection position to adaptively and dynamically correct low-level error information while strengthening the long-range spatial structure features established by the attention guidance module. These two modules can gradually establish and fuse the long-range semantic context. Furthermore, we propose a novel feature fusion method (FFM) to pass high-level semantics into the decoder. The experimental results show that our proposed method has better superiority, especially for capillaries with complex background and variable structure, and has more robust data adaptability than other state-of-the-art network structures in a small capacity. On multiple public datasets, it shows better stability. MRRM-Net can provide a theoretical basis for real-time detection technology of retinopathy.