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Abstract #3821

Segment Deep Gray Matter Nucleus from MR Images: An Automatic Computational Tool for Early Diagnosis of Parkinson’s Disease

Pei Dong1, Yanrong Guo1, Yue Gao2, Peipeng Liang3, Yonghong Shi4,5, Qian Wang6, Dinggang Shen1, and Guorong Wu1

1Department of Radiology and BRIC, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, 2School of Software, Tsinghua University, Beijing, People's Republic of China, 3Department of Radiology, Capital Medical University, Beijing, People's Republic of China, 4School of Basic Medical Sciences, Fudan University, Shanghai, People's Republic of China, 5Shanghai Key Laboratory of Medical Imaging Computing and Computer-Assisted Intervention, 6Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, People's Republic of China

Accurate and automatic brainstem nuclei segmentation from MR images plays an important role in seeking for imaging-biomarkers of Parkinson’s disease (PD). To address the segmentation challenge from regular MR images, we propose a novel multi-atlas patch based label fusion method where we use hyper-graph technique to handle the low image contrast issue. Our proposed method is successfully applied to a set of MR images from PPMI (Parkinson’s Progression Markers Initiative) dataset, and we have achieved significant improvements in terms of segmentation accuracy compared to the state-of-the-art methods.

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