Abstract #3521

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

Daniel Ramsing Lund^1,2, Mette Tøttrup Gade^1,2, Tina Jensen^1,2, Thomas B Shaw², Maciej Plocharski¹, Lasse Riis Østergaard¹, Steffen Bollmann², and Markus Barth^2,3

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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