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 Shi^4,5, 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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