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Abstract #2091

Pre-training and training of a Convolutional Neural Network for automatic and accurate hippocampus segmentation from T1-weighted MRI datasets

Samaneh Nobakht1, Nils Forkert2, Sean Nestor3, Sandra Black4, and Phillip Barber5

1Medical Sciences, University of Calgary, Calgary, AB, Canada, 2Radiology, University of Calgary, Calgary, AB, Canada, 3Psychiatry, University of Toronto, Toronto, ON, Canada, 4Medicine, Neurology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada, 5Clinical Neurosciences, University of Calgary, Calgary, AB, Canada

The hippocampus atrophy rate (volumetric loss per year) might be a good biomarker for predicting disease progression. However, hippocampus atrophy rate assessment requires accurate delineation of the structure from longitudinal scans. In this work, we propose an automatic approach based on convolutional neural network (CNN) for robust and reliable hippocampus segmentation. Therefore, the CNN was pre-trained using weakly annotated T1-weighted MRI datasets and fine-tuned using fully-annotated datasets. Leave-one-out cross validation revealed that the proposed method leads to robust and reproducible segmentation results with an average Dice coefficient of 0.89.

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