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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here