论文标题
使用多模式MR成像的HNF-NETV2用于脑肿瘤分割
HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging
论文作者
论文摘要
在我们以前的工作中，$，即$，HNF-NET，高分辨率特征表示和轻巧的非本地自我发项机制，用于使用多模式MR成像进行用于脑肿瘤的分割。在本文中，我们通过添加尺度和尺度的语义歧视增强块来进一步利用全球语义歧视来为获得的高分辨率特征，将HNF-NET扩展到HNF-NETV2。我们在多模式脑肿瘤分割挑战（BRATS）2021数据集方面训练和评估了HNF-NETV2。 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.我们的方法赢得了RSNA 2021脑肿瘤AI挑战奖（分段任务），该奖项在所有1250个提交结果中排名第八。
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.
