  In this paper, we propose a Hybrid High-resolution and Non-local Feature
Network (H2NF-Net) to segment brain tumor in multimodal MR images. Our H2NF-Net
uses the single and cascaded HNF-Nets to segment different brain tumor
sub-regions and combines the predictions together as the final segmentation. We
trained and evaluated our model on the Multimodal Brain Tumor Segmentation
Challenge (BraTS) 2020 dataset. The results on the test set show that the
combination of the single and cascaded models achieved average Dice scores of
0.78751, 0.91290, and 0.85461, as well as Hausdorff distances ($95\%$) of
26.57525, 4.18426, and 4.97162 for the enhancing tumor, whole tumor, and tumor
core, respectively. Our method won the second place in the BraTS 2020 challenge
segmentation task out of nearly 80 participants.
