  Accurate segmentation of MR brain tissue is a crucial step for
diagnosis,surgical planning, and treatment of brain abnormalities. However,it
is a time-consuming task to be performed by medical experts. So, automatic and
reliable segmentation methods are required. How to choose appropriate training
dataset from limited labeled dataset rather than the whole also has great
significance in saving training time. In addition, medical data labeled is too
rare and expensive to obtain extensively, so choosing appropriate unlabeled
dataset instead of all the datasets to annotate, which can attain at least same
performance, is also very meaningful. To solve the problem above, we design an
automatic segmentation method based on U-shaped deep convolutional network and
obtain excellent result with average DSC metric of 0.8610, 0.9131, 0.9003 for
Cerebrospinal Fluid (CSF), Gray Matter (GM) and White Matter (WM) respectively
on the well-known IBSR18 dataset. We use bootstrapping algorithm for selecting
the most effective training data and get more state-of-the-art segmentation
performance by using only 50% of training data. Moreover, we propose a strategy
of MR brain tissue images' suggestive annotation for unlabeled medical data
based on the modified U-net. The proposed method performs fast and can be used
in clinical.
