  Medical image segmentation is challenging especially in dealing with small
dataset of 3D MR images. Encoding the variation of brain anatomical struc-tures
from individual subjects cannot be easily achieved, which is further
chal-lenged by only a limited number of well labeled subjects for training. In
this study, we aim to address the issue of brain MR image segmentation in small
da-taset. First, concerning the limited number of training images, we adopt
adver-sarial defense to augment the training data and therefore increase the
robustness of the network. Second, inspired by the prior knowledge of neural
anatomies, we reorganize the segmentation tasks of different regions into
several groups in a hierarchical way. Third, the task reorganization extends to
the semantic level, as we incorporate an additional object-level classification
task to contribute high-order visual features toward the pixel-level
segmentation task. In experiments we validate our method by segmenting gray
matter, white matter, and several major regions on a challenge dataset. The
proposed method with only seven subjects for training can achieve 84.46% of
Dice score in the onsite test set.

Image and Video Processing;Computer Vision and Pattern Recognition