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.

本论文提出了一种混合高分辨率和非局部特征网络（H2NF-Net）来在多模态MR图像中对脑肿瘤进行分割，使用单一和级联的HNF-Nets来分割不同的脑肿瘤亚区域，并将预测合并作为最终分割。在BraTS 2020数据集上训练和评估模型，结果表明单一和级联模型的组合分别达到了增强肿瘤、整个肿瘤和肿瘤核的平均Dice分数为0.78751、0.91290和0.85461，以及Hausdorff距离（95%）分别为26.57525、4.18426和4.97162。我们的方法在BraTS 2020挑战中的分割任务中获得了第二名。

H2NF-Net应用多模态MR成像用于脑肿瘤分割：作为2020 BraTS挑战赛分割任务的第二名解决方案