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In our previous work, $i.e.$, HNF-Net, high-resolution feature representationand light-weight non-local self-attention mechanism are exploited for braintumor segmentation using multi-modal MR imaging. In this paper, we extend ourHNF-Net to HNF-Netv2 by adding inter-scale and intra-scale semanticdiscrimination enhancing blocks to further exploit global semanticdiscrimination for the obtained high-resolution features. We trained andevaluated our HNF-Netv2 on the multi-modal Brain Tumor Segmentation Challenge(BraTS) 2021 dataset. The result on the test set shows that our HNF-Netv2achieved the average Dice scores of 0.878514, 0.872985, and 0.924919, as wellas the Hausdorff distances ($95\%$) of 8.9184, 16.2530, and 4.4895 for theenhancing tumor, tumor core, and whole tumor, respectively. Our method won theRSNA 2021 Brain Tumor AI Challenge Prize (Segmentation Task), which ranks 8thout of all 1250 submitted results.

HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging