White matter hyperintensities volume and cognition: Assessment of a deep learning based lesion detection and quantification algorithm on the Alzheimers Disease Neuroimaging Initiative
Image and Video Processing2020-12-25v1Computer Vision and Pattern RecognitionMachine LearningQuantitative Methods
The relationship between cognition and white matter hyperintensities (WMH) volumes often depends on the 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 volumes 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 that larger WMH volumes correlated with worse performance on executive function (P=.004), memory (P=.01), and language (P=.005).
@article{arxiv.2012.13059,
title = {White matter hyperintensities volume and cognition: Assessment of a deep learning based lesion detection and quantification algorithm on the Alzheimers Disease Neuroimaging Initiative},
author = {Lavanya Umapathy and Gloria Guzman Perez-Carillo and Blair Winegar and Srinivasan Vedantham and Maria Altbach and Ali Bilgin},
journal= {arXiv preprint arXiv:2012.13059},
year = {2020}
}
Comments
3 pages, 4 figures, Submitted to The Annual Conference of International Society of Magnetic Resonance in Medicine (2021)