Two-Stage Cascaded U-Net: 1st Place Solution to BraTS Challenge 2019 Segmentation Task

In this paper, we devise a novel two-stage cascaded U-Net to segment the substructures of brain tumors from coarse to fine. The network is trained end-to-end on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2019 training dataset. Experimental results on the testing set demonstrate that the proposed method achieved average Dice scores of 0.83267, 0.88796 and 0.83697, as well as Hausdorff distances (95%) of 2.65056, 4.61809 and 4.13071, for the enhancing tumor, whole tumor and tumor core, respectively. The approach won the 1st place in the BraTS 2019 challenge segmentation task, with more than 70 teams participating in the challenge.