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Many functional and structural neuroimaging studies call for accuratemorphometric segmentation of different brain structures starting from imageintensity values of MRI scans. Current automatic (multi-) atlas-basedsegmentation strategies often lack accuracy on difficult-to-segment brainstructures and, since these methods rely on atlas-to-scan alignment, they maytake long processing times. Recently, methods deploying solutions based onConvolutional Neural Networks (CNNs) are making the direct analysis ofout-of-the-scanner data feasible. However, current CNN-based solutionspartition the test volume into 2D or 3D patches, which are processedindependently. This entails a loss of global contextual information therebynegatively impacting the segmentation accuracy. In this work, we design andtest an optimised end-to-end CNN architecture that makes the exploitation ofglobal spatial information computationally tractable, allowing to process awhole MRI volume at once. We adopt a weakly supervised learning strategy byexploiting a large dataset composed by 947 out-of-the-scanner (3 TeslaT1-weighted 1mm isotropic MP-RAGE 3D sequences) MR Images. The resulting modelis able to produce accurate multi-structure segmentation results in only fewseconds. Different quantitative measures demonstrate an improved accuracy ofour solution when compared to state-of-the-art techniques. Moreover, through arandomised survey involving expert neuroscientists, we show that subjectivejudgements clearly prefer our solution with respect to the widely adoptedatlas-based FreeSurfer software.

CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for  weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner  MRI