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Skull stripping is usually the first step for most brain analysisprocess inmagnetic resonance images. A lot of deep learn-ing neural network based methodshave been developed toachieve higher accuracy. Since the 3D deep learningmodelssuffer from high computational cost and are subject to GPUmemory limitchallenge, a variety of 2D deep learning meth-ods have been developed. However,existing 2D deep learn-ing methods are not equipped to effectively capture 3Dse-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 the3D context information.For the context-encoding method, firstly we encode the2Dfeatures of original 2D network, secondly we encode the sub-volume of 3D MRIimages, finally we fuse the encoded 2Dfeatures and 3D features with semanticencoding classifica-tion loss. To get computational efficiency, although ween-code the sub-volume of 3D MRI images instead of buildinga 3D neural network,extensive experiments on three bench-mark Datasets demonstrate our method canachieve superioraccuracy to state-of-the-art alternative methods with thedicescore 99.6% on NFBS and 99.09 % on LPBA40 and 99.17 %on OASIS.

Context-endcoding for neural network based skull stripping in magnetic resonance imaging