2019 Conference on Cognitive Computational Neuroscience

13-16 September 2019    Berlin, Germany

Technical Program

Paper Detail

Paper: PS-1B.33
Session: Poster Session 1B
Location: H Fläche 1.OG
Session Time: Saturday, September 14, 16:30 - 19:30
Presentation Time:Saturday, September 14, 16:30 - 19:30
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Fast Brain MRI Segmentation Using a Volumetric Deep Learning Approach
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
DOI: https://doi.org/10.32470/CCN.2019.1220-0
Authors: Dennis Bontempi, Sergio Benini, Alberto Signoroni, University of Brescia, Italy; Lars Muckli, Michele Svanera, University of Glasgow, United Kingdom
Abstract: Functional and Structural MRI studies benefit from good segmentation of grey and white matter, for example to allow for cortex-based alignment. Automatic segmentation tools apply (multi-) atlas-based segmentation strategies that often lack the accuracy on difficult-to-segment brain structures and take several hours of processing. Moreover, these algorithm depend on aligning scans and atlases. Alternatively, to avoid this last step, many methods nowadays deploy solutions based on Convolutional Neural Networks (CNNs), by which the testing volume is partitioned into 2D or 3D patches processed independently. This entails a loss of global contextual information thereby negatively impacting the final accuracy of the segmented structures. To fully exploit global spatial information, we introduce a CNN-based segmentation algorithm that processes the whole MRI volume at once and produces an accurate result in only few seconds starting from a single MRI sequence (T1w). Training and testing are performed on 947 out-of-the-scanner MRI volumes acquired using a standard 1mm-isotropic MPRAGE sequence (3T). Results are evaluated using the Dice Similarity Coefficient and the Hausdorff Distance. The comparison with the state of the art shows that our method outperforms any other current CNN-based solution.