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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 Bilgin1,4,5
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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