GLoMS-Net: A Global-local Interactive Multi-scale Network for Perivascular Spaces Segmentation

Perivascular spaces (PVSs) segmentation algorithms based on deep learning have made steady progress, while these methods are usually limited by the information loss on thin tubular structure of PVSs during downsampling and the diffculty in capturing various morphologies. Addressing these challenges, we propose a global-local interactive multi-scale network (GLoMS-Net) based on the hybrid of convolutional neural network and transformer architecture for automatic PVSs segmentation. Specifically, global imaging features are extracted using a Transformer-based sub-network, and multi-level local features are extracted using the encoder of the TransUNet architecture. Afterward, the global and multi-level local features are interacted and these interactive features are fed into the decoder of the TransUNet via skip connections to compensate the information loss due to downsampling. Furthermore, we use the multi-scale feature learning module and the multi-scale fusion strategy to capture comprehensive contextual features of PVSs. The experimental results on 3T brain MR images from 56 participants with 168 slices demonstrate the superior performance of the proposed GLoMS-Net compared with several state-of-the-art methods for PVSs segmentation.