Abstract #2791

Deep learning-based quantification of white matter hyperintensity applicable to real-world clinical FLAIR images

Kengo Onda¹, Jill Chotiyanonta¹, Yuto Uchida¹, Xin Li¹, Susumu Mori^1,2, and Kenichi Oishi^1,2,3

¹The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, ²F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, ³The Richman Family Precision Medicine Center of Excellence in Alzheimer’s Disease, The Johns Hopkins University School of Medicine, Baltimore, MD, United States

Synopsis

Keywords: White Matter, Neurodegeneration, White Matter HyperintensityWhite matter hyperintensity (WMH) in the brain is known to correlate with cognitive prognosis in many diseases; automated quantification tools for WMH have been developed, but most have been used to quantify study data from specific diseases imaged with a single scanning protocol. The low accuracy of these tools when used for clinical data with diverse scan protocols and diseases has been a problem in clinical applications. To overcome this limitation, we developed a deep-learning-based WMH quantification model for real-world clinical FLAIR images with high heterogeneity. The results show the potential of this method as a clinical tool.

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