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We present a method to address the challenging problem of segmentation ofmulti-modality isointense infant brain MR images into white matter (WM), graymatter (GM), and cerebrospinal fluid (CSF). Our method is based oncontext-guided, multi-stream fully convolutional networks (FCN), which aftertraining, can directly map a whole volumetric data to its volume-wise labels.In order to alleviate the poten-tial gradient vanishing problem duringtraining, we designed multi-scale deep supervision. Furthermore, contextinfor-mation was used to further improve the performance of our method.Validated on the test data of the MICCAI 2017 Grand Challenge on 6-month infantbrain MRI segmentation (iSeg-2017), our method achieved an average Dice OverlapCoefficient of 95.4%, 91.6% and 89.6% for CSF, GM and WM, respectively.

Multi-stream 3D FCN with Multi-scale Deep Supervision for Multi-modality Isointense Infant Brain MR Image Segmentation