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

Dennis Bontempi, Sergio Benini, Alberto Signoroni, Michele Svanera, Lars Muckli

Abstract: Highlights • The first fully-volumetric CNN based approach for multi-structure brain MRI segmentation. • Architecture designed to enable the processing of a whole MRI volume without introducing any partitioning. • Enables leveraging of both local (voxel-level) and global (spatial) features. • Weakly-supervised training (exploiting automatic atlas-based segmentation) on large pool of out-of-the-scanner volumes (900 brain scans). • Experimental results, both quantitative (comparison with state-of-the-art approaches) and qualitative (survey of experts), supports the adoption of our approach. Abstract Many functional and structural neuroimaging studies call for accurate morphometric segmentation of different brain structures starting from image intensity values of MRI scans. Current automatic (multi-) atlas-based segmentation strategies often lack accuracy on difficult-to-segment brain structures and, since these methods rely on atlas-to-scan alignment, they may take long processing times. Alternatively, recent methods deploying solutions based on Convolutional Neural Networks (CNNs) are enabling the direct analysis of out-of-the-scanner data. However, current CNN-based solutions partition the test volume into 2D or 3D patches, which are processed independently. This process entails a loss of global contextual information, thereby negatively impacting the segmentation accuracy. In this work, we design and test an optimised end-to-end CNN architecture that makes the exploitation of global spatial information computationally tractable, allowing to process a whole MRI volume at once. We adopt a weakly supervised learning strategy by exploiting a large dataset composed of 947 out-of-the-scanner (3 Tesla T1-weighted 1mm isotropic MP-RAGE 3D sequences) MR Images. The resulting model is able to produce accurate multi-structure segmentation results in only a few seconds. Different quantitative measures demonstrate an improved accuracy of our solution when compared to state-of-the-art techniques. Moreover, through a randomised survey involving expert neuroscientists, we show that subjective judgements favour our solution with respect to widely adopted atlas-based software.