论文标题
使用级联的3D密集连接的U-NET进行脑肿瘤分割
Brain tumour segmentation using cascaded 3D densely-connected U-net
论文作者
论文摘要
准确的脑肿瘤分割是改善疾病诊断和适当治疗计划的关键步骤。在本文中，我们提出了一种基于深度学习的方法，以将脑肿瘤分割为其子区域：整个肿瘤，肿瘤核心和增强肿瘤。所提出的体系结构是基于Ronneberger等人U-NET体系结构的变体的3D卷积神经网络。 [17]具有三个主要修改：（i）使用残留块（ii）使用密集块而不是跳过连接的较重编码器，轻度解码器结构，以及（iii）在网络解码器部分中使用自复杂的部分。使用两种不同的方法对网络进行了训练和测试：一个多任务框架，以同时分割所有肿瘤子区域，而三个阶段的级联框架一次分割一个子区域。还计算了两个框架的结果集合。为了解决班级不平衡问题，在预处理步骤中采用了适当的补丁提取。在后处理步骤中使用了连接的组件分析，以减少假阳性预测。 BRATS20验证数据集的实验结果表明，整个肿瘤，肿瘤核心和增强肿瘤的平均骰子得分分别为0.90、0.82和0.78。
Accurate brain tumour segmentation is a crucial step towards improving disease diagnosis and proper treatment planning. In this paper, we propose a deep-learning based method to segment a brain tumour into its subregions: whole tumour, tumour core and enhancing tumour. The proposed architecture is a 3D convolutional neural network based on a variant of the U-Net architecture of Ronneberger et al. [17] with three main modifications: (i) a heavy encoder, light decoder structure using residual blocks (ii) employment of dense blocks instead of skip connections, and (iii) utilization of self-ensembling in the decoder part of the network. The network was trained and tested using two different approaches: a multitask framework to segment all tumour subregions at the same time and a three-stage cascaded framework to segment one sub-region at a time. An ensemble of the results from both frameworks was also computed. To address the class imbalance issue, appropriate patch extraction was employed in a pre-processing step. The connected component analysis was utilized in the post-processing step to reduce false positive predictions. Experimental results on the BraTS20 validation dataset demonstrates that the proposed model achieved average Dice Scores of 0.90, 0.82, and 0.78 for whole tumour, tumour core and enhancing tumour respectively.
