  Skull stripping is usually the first step for most brain analysisprocess in
magnetic resonance images. A lot of deep learn-ing neural network based methods
have been developed toachieve higher accuracy. Since the 3D deep learning
modelssuffer from high computational cost and are subject to GPUmemory limit
challenge, a variety of 2D deep learning meth-ods have been developed. However,
existing 2D deep learn-ing methods are not equipped to effectively capture 3D
se-mantic information that is needed to achieve higher accuracy.In this paper,
we propose a context-encoding method to em-power the 2D network to capture the
3D context information.For the context-encoding method, firstly we encode the
2Dfeatures of original 2D network, secondly we encode the sub-volume of 3D MRI
images, finally we fuse the encoded 2Dfeatures and 3D features with semantic
encoding classifica-tion loss. To get computational efficiency, although we
en-code the sub-volume of 3D MRI images instead of buildinga 3D neural network,
extensive experiments on three bench-mark Datasets demonstrate our method can
achieve superioraccuracy to state-of-the-art alternative methods with the
dicescore 99.6% on NFBS and 99.09 % on LPBA40 and 99.17 %on OASIS.
