Abstract #1863

Deep-Learning based Segmentation and Localization of White Matter Hyperintensities on Magnetic Resonance Images

Wenhao Jiang^1,2, Jian Zhang¹, Peng Cao³, Jing Gu¹, and Silun Wang¹

¹YiWei Medical Technology Co.,Ltd, Shenzhen, China, ²School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China, ³Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, Hong Kong

In this work, deep-learning-based methods were utilized to segment white matter hyperintensities (WMH) on T2-weighted MRI from 213 patients diagnosed with ischemia and lacune. Vulnerability maps of each disease were generated regarding the prevalence of WMH registered to the standard MNI template. The WMH were allocated into 68 regions of interest using a Hammers atlas. Correlation among the region-specific WMH was analyzed for a lesion-symptom study.

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