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Abstract #1787

Evaluating VoxelMorph, a deep learning-based non-linear diffeomorphic registration algorithm, against native ANTs SyN

Victoria Madge1,2, Philip Novosad1,2, Daniel A. Di Giovanni1,3, and D. Louis Collins1,2,3
1McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada, 2Department of Biomedical Engineering, McGill University, Montreal, QC, Canada, 3Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada

VoxelMorph is a deep-learning based non-linear diffeomorphic registration algorithm which claims to perform comparably to the state-of-the-art. However, the previous evaluation did not compare against manual gold-standard anatomical segmentations, used only the Dice metric for comparison, and compared against a modified version of a state-of-the-art algorithm, ANTs SyN. Here, VoxelMorph is evaluated against an unmodified version of ANTs SyN using multiple metrics based on manual labels. Results show VoxelMorph is less robust than ANTs SyN and underperforms in the presence of simulated deformations, and in registration of BrainWeb20 images to the VoxelMorph atlas.

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