Vision graph u-net: geometric learning enhanced encoder for medical image segmentation and restoration

Convolutional neural networks (CNNs) are known for their powerful feature extraction ability, and have achieved great success in a variety of image processing tasks. However, convolution filters only extract local features and neglect long-range self-similarity information, which is the vital prior information commonly existing in image data. To this end, we put forward a new backbone neural network: vision graph U-Net (VGU-Net), which is the first model to construct multi-scale graph structures through the hierarchical down-sampling layers of the U-Net architecture. The graph structure is constructed by the self-attention mechanism. By replacing CNNs in the bottleneck layer and skip connection layers with the graph convolution networks (GCNs), the multi-scale graph structure visualization allows an interpretation of long-range interactions. We extend the VGU-Net backbone model for the widely considered compressed sensing MR image reconstruction task and propose a knowledge-driven deep unrolling scheme based on the half-quadratic splitting algorithm, which combines the interpretability of knowledge-driven model with the versatility of data-driven deep learning method to achieve remarkable reconstruction results. Moreover, we verify the segmentation ability of the VGU-Net backbone model on the multi-modality brain tumor segmentation dataset and white blood cell image segmentation dataset, and both achieve state-of-the-art performance. The code is publicly available at https://github.com/jyh6681/VGU-Net.