Learning geometric and visual features for medical image segmentation with vision GNN

As a fundamental task, medical image segmentation plays a crucial role in various clinical applications. In recent years, deep learning-based segmentation methods have achieved significant success. However, these methods typically represent the image and objects within it as grid-structural data, while insufficient attention is given to relationships between the objects to segment. To address this issue, we propose a novel model called MedSegViG, which consists of a hierarchical encoder based on Vision GNN (ViG) and a hybrid feature decoder. During the segmentation process, our model first represents the image as a graph and then utilizes the encoder to extract multi-level graph features and image features. Finally, our hybrid feature decoder fuses these features to generate the final segmentation map. To validate the effectiveness of the proposed model, we conducted extensive experiments on six datasets across three types of lesions: polyps, skin lesions, and retinal vessels. The results demonstrate that MedSegViG achieves superior segmentation accuracy, robustness, and generalizability.