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

Daniel Ramsing Lund, Mette Tøttrup Gade, Tina Jensen, Thomas B. Shaw, Maciej Plocharski, Lasse Riis Østergaard, Steffen Bollmann, Markus Barth

Research output: Contribution to book/anthology/report/conference proceedingConference abstract in proceedingResearchpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 2020 ISMRM & SMRT Virtual Conference & Exhibition, 08-14 August 2020
Publication date24 Jul 2020
Article number3521
Publication statusPublished - 24 Jul 2020
Event2020 ISMRM & SMRT Virtual Conference & Exhibition -
Duration: 8 Aug 202014 Aug 2020

Conference

Conference2020 ISMRM & SMRT Virtual Conference & Exhibition
Period08/08/202014/08/2020

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