HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging
This work addresses brain tumor segmentation for medical imaging applications, presenting an incremental improvement over prior methods.
The authors tackled brain tumor segmentation from multi-modal MR imaging by extending their previous HNF-Net to HNF-Netv2 with added semantic discrimination blocks, achieving average Dice scores of 0.878514, 0.872985, and 0.924919 for different tumor regions and ranking 8th out of 1250 submissions in a challenge.
In our previous work, $i.e.$, HNF-Net, high-resolution feature representation and light-weight non-local self-attention mechanism are exploited for brain tumor segmentation using multi-modal MR imaging. In this paper, we extend our HNF-Net to HNF-Netv2 by adding inter-scale and intra-scale semantic discrimination enhancing blocks to further exploit global semantic discrimination for the obtained high-resolution features. We trained and evaluated our HNF-Netv2 on the multi-modal Brain Tumor Segmentation Challenge (BraTS) 2021 dataset. The result on the test set shows that our HNF-Netv2 achieved the average Dice scores of 0.878514, 0.872985, and 0.924919, as well as the Hausdorff distances ($95\%$) of 8.9184, 16.2530, and 4.4895 for the enhancing tumor, tumor core, and whole tumor, respectively. Our method won the RSNA 2021 Brain Tumor AI Challenge Prize (Segmentation Task), which ranks 8th out of all 1250 submitted results.