  Convolutional neural networks (CNN) and Transformer variants have emerged as
the leading medical image segmentation backbones. Nonetheless, due to their
limitations in either preserving global image context or efficiently processing
irregular shapes in visual objects, these backbones struggle to effectively
integrate information from diverse anatomical regions and reduce
inter-individual variability, particularly for the vasculature. Motivated by
the successful breakthroughs of graph neural networks (GNN) in capturing
topological properties and non-Euclidean relationships across various fields,
we propose NexToU, a novel hybrid architecture for medical image segmentation.
NexToU comprises improved Pool GNN and Swin GNN modules from Vision GNN (ViG)
for learning both global and local topological representations while minimizing
computational costs. To address the containment and exclusion relationships
among various anatomical structures, we reformulate the topological interaction
(TI) module based on the nature of binary trees, rapidly encoding the
topological constraints into NexToU. Extensive experiments conducted on three
datasets (including distinct imaging dimensions, disease types, and imaging
modalities) demonstrate that our method consistently outperforms other
state-of-the-art (SOTA) architectures. All the code is publicly available at
https://github.com/PengchengShi1220/NexToU.
