Abstract #3485

White matter hyperintensity volumes and cognition: Assessment of a deep learning-based lesion detection and quantification algorithm on ADNI

Lavanya Umapathy¹, Gloria Guzman Perez-Carillo², Blair Winegar³, Srinivasan Vedantham⁴, Maria Altbach⁴, and Ali Bilgin^1,4,5

¹Electrical and Computer Engineering, University of Arizona, Tucson, AZ, United States, ²Mallinckrodt Institute of Radiology, St Louis, MO, United States, ³Radiology and Imaging Sciences, University of Utah, Salt Lake, UT, United States, ⁴Medical Imaging, University of Arizona, Tucson, AZ, United States, ⁵Biomedical Engineering, University of Arizona, Tucson, AZ, United States

The relationship between cognition and white matter hyperintensities (WMH) volumes often depends on accuracy of the lesion segmentation algorithm used. As such, accurate detection and quantification of WMH is of great interest. Here, we use a deep learning-based WMH segmentation algorithm, StackGen-Net, to detect and quantify WMH on 3D-FLAIR images from ADNI. We used a subset of subjects (n=20) and obtained manual WMH segmentations by an experienced neuro-radiologist to demonstrate the accuracy of our algorithm. On a larger cohort of subjects (n=290), we observed larger WMH volumes correlated with worse performance on executive function (P=.004), memory (P=.01), and language (P=.005).

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