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Abstract #1863

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

Wenhao Jiang1,2, 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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