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Abstract #3521

Multi-Contrast Hippocampal Subfield Segmentation for Ultra-High Field 7T MRI Data using Deep Learning

Daniel Ramsing Lund1,2, Mette Tøttrup Gade1,2, Tina Jensen1,2, Thomas B Shaw2, Maciej Plocharski1, Lasse Riis Østergaard1, Steffen Bollmann2, and Markus Barth2,3
1Department of Health Science and Technology, Aalborg University, Aalborg, Denmark, 2Centre for Advanced Imaging, University of Queensland, Brisbane, Australia, 3School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia

Ultra-high field 7T MRI and the utilization of multiple MRI contrasts potentially enable a superior hippocampal subfield segmentation. A residual-dense fully convolutional neural network based on U-net, including a dilated-convolutional-block was implemented for hippocampal subfield segmentation. Two data sets were combined for training and mean DSC of 0.7723 was obtained. DSC was higher for larger subfields, which were undersegmented, while smaller subfields were oversegmented. Results were comparable to the atlas-based method ASHS, while providing a substantially faster processing time.

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