  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.
