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 Anderson^5,6, Maxime Descoteaux², Bennett A Landman^5,6,7,8, 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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