  Accurate segmentation of MR brain tissue is a crucial step for diagnosis,
surgical planning, and treatment of brain abnormalities. Automatic and reliable
segmenta-tion methods are required to assist doctor. Over the last few years,
deep learning especially deep convolutional neural networks (CNNs) have emerged
as one of the most prominent approaches for image recognition problems in
various do-mains. But the improvement of deep networks always needs
inspiration, which is rare for the ordinary. Until now,there have been
reasonable MR brain tissue segmentation methods,all of which can achieve
promising performance. These different methods have their own characteristic
and are distinctive for data sets. In other words, different models performance
vary widely on the same data sets and each model has what it is skilled in. It
is on the basis of this, we propose a judgement to distinguish data sets that
different models are good at. With our method, the segmentation accuracy can be
improved easily based on the existing models, neither without increasing
training data nor improving the network. We validate our method on the widely
used IBSR 18 dataset and obtain average dice ratio of 88.06%,while it is 85.82%
and 86.92% when only using separate one model respectively.
