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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