论文标题
H2NF-NET使用多模式MR成像用于脑肿瘤分割：第2位解决方案挑战2020分割任务
H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task
论文作者
论文摘要
在本文中，我们提出了一个混合高分辨率和非本地特征网络（H2NF-NET），以分割多模式MR图像中的脑肿瘤。我们的H2NF-NET使用单个和级联的HNF-NET来分割不同的脑肿瘤子区域，并将预测结合在一起作为最终分割。我们培训并评估了有关多模式脑肿瘤分割挑战（BRAT）2020数据集的模型。测试集的结果表明，单个和级联模型的组合达到的平均骰子得分为0.78751、0.91290和0.85461，以及Hausdorff距离（95美元\％$）的26.57525，4.18426，以及4.97162的距离，用于增强的TUMOR和TUMOR，整个TUMOR，整个TUMOOR，整个TUMOOR。我们的方法赢得了近80名参与者的2020挑战细分任务中的第二名。
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.
