Exploiting Cross-modal Collaboration and Discrepancy for Semi-supervised Ischemic Stroke Lesion Segmentation from Multi-sequence MRI Images.

Accurate ischemic stroke lesion segmentation is useful to define the optimal reperfusion treatment and unveil the stroke etiology. Despite the importance of diffusion-weighted MRI (DWI) for stroke diagnosis, learning from multi-sequence MRI images like apparent diffusion coefficient (ADC) can capitalize on the complementary nature of information from various modalities and show strong potential to improve the performance of segmentation. However, existing deep learning-based methods require large amounts of well-annotated data from multiple modalities for training, while acquiring such datasets is often impractical. We conduct the exploration of semi-supervised stroke lesion segmentation from multi-sequence MRI images by utilizing unlabeled data to improve performance using limited annotation and propose a novel framework by exploiting cross-modality collaboration and discrepancy to efficiently utilize unlabeled data. Specifically, we adopt a cross-modal bidirectional copy-paste strategy to enable information collaboration between different modalities and a cross-modal discrepancy-informed correction strategy to efficiently learn from limited labeled multi-sequence MRI data and abundant unlabeled data. Extensive experiments on the ischemic stroke lesion segmentation (ISLES 22) dataset demonstrate that our method efficiently utilizes unlabeled data with 12.32% DSC improvements compared with a supervised baseline using 10% annotations and outperforms existing semi-supervised segmentation methods with better performance.