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Abstract #1022

Learning white matter fingerprints from structural information

Colin Hansen1, Qi Yang1, Francois Rheault2, Bramsh Qamar3, Owen Williams4, Susan Resnick4, Eleftherios Garyfallidis3, Adam W Anderson5,6, Maxime Descoteaux2, Bennett A Landman5,6,7,8, and Kurt G Schilling5
1Computer Science, Vanderbilt University, Nashville, TN, United States, 2Sherbrooke Connectivity Imaging Laboratory (SCIL), Universite de Sherbrooke, Sherbrooke, QC, Canada, 3Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, IN, United States, 4Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, United States, 5Vanderbilt University Institute of Imaging Science, Nashville, TN, United States, 6Biomedical Engineering, Vanderbilt University, Nashville, TN, United States, 7Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States, 8Electrical Engineering, Vanderbilt University, Nashville, TN, United States

Here, we present a tool and reconstruction method to label white matter pathways directly on structural images without the need for diffusion MRI or tractography. A 3D U-net was trained utilizing 1109 scan sessions where fiber pathways were segmented using two different segmentation schemes. Results on testing datasets show anatomically viable segmentations and moderate-to-high volume overlaps with gold-standard pathways, on par with scan-rescan reproducibility of tractography on the same datasets. We envision the use of this tool for visualizing the expected course of white matter pathways when diffusion data are not available.

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