论文标题
混合网：用于大脑分割的多模式混合网络
MixNet: Multi-modality Mix Network for Brain Segmentation
论文作者
论文摘要
自动化的大脑结构分割对于许多临床定量分析和诊断至关重要。在这项工作中，我们介绍了Mixnet，这是一种2D语义深度卷积神经网络，以分割多模式MRI图像中的大脑结构。该网络由我们修改的深层学习单元组成。在单元中，我们用扩张的卷积层代替了传统的卷积层，该卷积层避免了使用合并层和反向倾斜层的使用，从而减少了网络参数的数量。最终预测是通过从多个量表和方式汇总信息来做出的。金字塔池模块用于捕获输出端的解剖结构的空间信息。此外，我们测试了三个体系结构（MixNETV1，MixNETV2和MixNETV3），它们以不同的方式融合方式，以查看对结果的影响。我们的网络实现了最新的性能。 MixNETV2在2018年MICCAI的MRBRAINS挑战赛中提交，并在三标签任务中赢得了第三名。在MRBRAINS2018数据集中，包括各种病理学的受试者，总的DSC（骰子系数）为84.7％（灰质），87.3％（白质）和83.4％（脑脊液）（脑脊液）仅获得7个受试者作为培训数据。
Automated brain structure segmentation is important to many clinical quantitative analysis and diagnoses. In this work, we introduce MixNet, a 2D semantic-wise deep convolutional neural network to segment brain structure in multi-modality MRI images. The network is composed of our modified deep residual learning units. In the unit, we replace the traditional convolution layer with the dilated convolutional layer, which avoids the use of pooling layers and deconvolutional layers, reducing the number of network parameters. Final predictions are made by aggregating information from multiple scales and modalities. A pyramid pooling module is used to capture spatial information of the anatomical structures at the output end. In addition, we test three architectures (MixNetv1, MixNetv2 and MixNetv3) which fuse the modalities differently to see the effect on the results. Our network achieves the state-of-the-art performance. MixNetv2 was submitted to the MRBrainS challenge at MICCAI 2018 and won the 3rd place in the 3-label task. On the MRBrainS2018 dataset, which includes subjects with a variety of pathologies, the overall DSC (Dice Coefficient) of 84.7% (gray matter), 87.3% (white matter) and 83.4% (cerebrospinal fluid) were obtained with only 7 subjects as training data.
