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Abstract #1965

Medical Image Registration Using Deep Learning Techniques Applied to Pediatric Magnetic Resonance Imaging (MRI) Brain Scans

Andjela Dimitrijevic1,2, Vincent Noblet3, and Benjamin De Leener1,2,4
1NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, QC, Canada, 2Research Center, Ste-Justine Hospital University Centre, Montréal, QC, Canada, 3ICube-UMR 7357, Université de Strasbourg, CNRS, Strasbourg, France, 4Computer Engineering and Software Engineering, Polytechnique Montréal, Montréal, QC, Canada


Deep learning techniques have a potential in allowing fast deformable registration tasks. Studies around registration often focus on adult populations, even if there is a need for pediatric research where less data and studies are being produced. In this study, we compared three methods for intra-subject registration on publicly available Calgary Preschool dataset. Using the DeepReg framework, pre-registering with a rigid and affine transformation (proposed RigidAffineReg method) showed the least negative JD values and the highest Dice score (0.924±0.045). By achieving faster alignments, this tool for pediatric MRI scans could help proliferate larger scale population research in brain developmental studies.

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