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Abstract #0018

AUTOMATED BRAIN TISSUE SEGMENTATION USING DEEP LEARNING AND IMPERFECT LABELING

Chandan Ganesh Bangalore Yogananda1, Benjamin C Wagner1, Gowtham K Murugesan1, Sahil S Nalawade1, Ananth J Madhurantakam1, and Joseph A Maldjian1

1Radiology, University of Texas Southwestern Medical Center, Dallas, TX, United States

This work presents a deep learning pipeline to perform brain tissue segmentation on T1w Magnetic Resonance images (MRI). Two separate 3D-Dense-Unets were designed: GW-net to segment the gray matter (GM) and white matter (WM) and CSF-net to segment the cerebrospinal fluid (CSF). The network was trained on T1w MRI from 785 datasets in the iTAKL study with their corresponding SPM12 segmentations as ground truth and tested on 50 held-out subjects from the iTAKL study, 50 subjects from the AADHS study and 131 subjects from the Human Connectome project (HCP). Our pipeline showed improved segmentations when tested on simulated data with known ground truth as compared to the existing neuroimaging packages including SPM12, FSL and CAT12.

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