Efficient Multi-modality MRI Fusion Based on Superpixel Method for Semi-supervised Brain Tumor Segmentation

Motivation: The multi-modal images can provide complementary information to improve automatic MRI segmentation performance. However, most multi-modal methods require long training times due to the complex network structures and the large amounts of multi-modal images involved. Furthermore, obtaining numerous labeled data is time-consuming and laborious. Goal(s): To achieve efficient and high-quality segmentation using only a few labeled data. Approach: We propose an efficient training-free multi-modal fusion strategy based on superpixel method for semi-supervised brain tumor segmentation. Results: The experiments on BraTS18 dataset show that our method results in superior overall performance, and can greatly reduce the time costs of doctors. Impact: The strategy of using superpixel method to accelerate the network training process can assist in the timely diagnosis and treatment of diseases in the clinic, and provides a new idea to simplify multi-modal information fusion.