【摘要】 针对脑肿瘤磁共振成像(magnetic resonance imaging, MRI)分割中样本缺少、类不平衡、小区域分割精度低等问题，本文提出了基于3D No-New U-Net的多尺度多任务深度学习算法TDDU-Net。首先，采用了一个编码器和三个不完全相同解码器的结构；其次，采用逆瓶颈结构设计的ConvXt模块作为解码器的前置处理，克服在部分核心区域解码时高层语义未被充分利用的不足；再次，在最底层编码器和解码器连接处增加广义特征处理模块InConvXt,保证全局特征的准确性，增强网络的稳定性；最后，在保证准确率的情况下，使用深度可分离卷积在适当位置减少网络的参数计算量。实验表明，预测分割结果在整体肿瘤区域(whole tumor, WT)、肿瘤核心区域(turmor core, TC)、增强肿瘤区域(enhancing tumor, ET)的Dice相似系数(Dice similarity coefficient, DSC)在BraTS18数据集中分别达到了0.907,0.847,0.807。本文方法较其他方法表现出色，能准确分割出MRI中较小的肿瘤区域。更多还原

【Abstract】 In order to solve nonnegligible problems in brain tumor magnetic resonance imaging(MRI) segmentation, such as few samples, class imbalance and low accuracy of small districts, this essay proposes a new multi-scale and multitask deep-learning algorithm called TDDU-Net based on 3D No-New U-Net.Firstly, this paper applies the structure with an encoder and three different decoders to the network.Next, the ConvXt module is a pre-processor of the original decoder with a reverse bottleneck structure in order to overcome the underutilization of high-level semantics when some core regions decode.Then, InConvXt is a generalized feature processing module at the bottom layer between the encoder and decoder to ensure the accuracy of the generalized features and enhance the stability of the network.Finally, the deepwise convolution is used to reduce the calculation amount of the network parameter at the appropriate location while ensuring accuracy.The experiments show that the Dice similarity coefficients(DSCs) of the predicted segmentation in the BraTS18 dataset reaching 0.907,0.847,0.807 in the whole tumor region(WT),the tumor core region(TC) and the enhancing tumor region(ET).The method performs better, which is helpful in segmenting the smaller tumor area in MRI.更多还原