论文标题
通过额外的分类网络增强MRI脑肿瘤分割
Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network
论文作者
论文摘要
脑肿瘤分割在医学图像分析中起着至关重要的作用。在最近的研究中，深度卷积神经网络（DCNN）在解决肿瘤分割任务方面非常有力。我们在本文中提出了一种新颖的培训方法，该方法通过向网络添加附加分类分支来增强分割结果。整个网络对多模式脑肿瘤分割挑战（BRATS）2020培训数据集进行了端到端训练。在Brats的验证集中，它的平均骰子得分分别为增强的肿瘤，整个肿瘤和肿瘤核心分别为78.43％，89.99％和84.22％。
Brain tumor segmentation plays an essential role in medical image analysis. In recent studies, deep convolution neural networks (DCNNs) are extremely powerful to tackle tumor segmentation tasks. We propose in this paper a novel training method that enhances the segmentation results by adding an additional classification branch to the network. The whole network was trained end-to-end on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 training dataset. On the BraTS's validation set, it achieved an average Dice score of 78.43%, 89.99%, and 84.22% respectively for the enhancing tumor, the whole tumor, and the tumor core.
