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Abstract #3492

Improved Automated Hippocampus Segmentation using Deep Neural Networks

Maximilian Sackl1, Alina Dima2, Christian Payer2, Darko Štern3, Reinhold Schmidt1, and Stefan Ropele1
1Department of Neurology, Medical University of Graz, Graz, Austria, 2Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria, 3Department of Biophysics, Medical University of Graz, Graz, Austria

Segmentation of the hippocampal formation on T1-weighted structural MR scans is a prerequisite for most imaging studies in Alzheimer’s disease. In this work, we evaluated the performance and accuracy of deep learning-based hippocampus segmentation combined with manual ground truth (GT) data that originates from high-resolution T2-weighted MR images. Results were evaluated against the GT-labels and compared to segmentation results obtained with FreeSurfer. All learning approaches outperformed FreeSurfer in terms of accuracy and speed, where experiments utilizing the T2-based GT-labels yielded the best results. Thus, using T2-weighted images for training a deep learning model can improve automated HC segmentation.

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