Cerebral Artery Segmentation with Limited Data: Using Hierarchical Transformers

Motivation: Transformer networks have demonstrated their effectiveness in both large-scale natural language processing and 2D image analysis tasks. However, their potential in 3D medical image analysis, particularly on small training datasets, remains unexplored. Goal(s): Leveraging transformer-based models to attain highly accurate cerebral artery segmentation in 3D TOF-MRA images. Approach: SwinUNETR with a hard example mining loss function to perform cerebral artery segmentation on the public dataset CAS2023. Results: We obtained an average Dice score of 0.844 and 0.889 for the stenosis area, a normalized Hausdorff distance of 0.888 and 0.8444 for the stenosis area, along with a weighted Dice and Hausdorff score of 0.867. Impact: We used fewer than 100 cases to train a transformer model for artery segmentation, indicating that transformers have the potential to replace CNNs in the processing of 3D TOF-MRA medical images, even with a small training dataset.