Meeting Banner
Abstract #4402

Deep Learning Based Brain Tissue Segmentation from Novel Diffusion Kurtosis Imaging Features

Fan Zhang1, Anna Breger2, Lipeng Ning1, Carl-Fredrik Westin1, Lauren J O'Donnell1, and Ofer Pasternak1
1Harvard Medical School, Boston, MA, United States, 2University of Vienna, Wien, Austria

Brain tissue segmentation is important in many diffusion MRI (dMRI) visualization and quantification tasks. We propose a deep learning tissue segmentation method that relies only on dMRI data. We leverage diffusion kurtosis imaging (DKI) and a recently proposed mean-kurtosis-curve (MK-curve) method to create a feature set that is highly discriminative between different types of tissues. We train a Unet model with a recently developed augmented target loss function on dMRI data from the Human Connectome Project. We show improved segmentation performance compared to several other methods and reliable segmentation results when applied on data with a different acquisition.

This abstract and the presentation materials are available to 2020 meeting attendees and eLibrary customers only; a login is required.

Join Here