Abstract #0701

A seed point discontinuity-based level set method for accurate substantia nigra and red nucleus segmetation in QSM images

Tian Guo¹, Binshi Bo¹, Xinxin Zhao¹, Xu Yan², Yang Song¹, Caixia Fu³, Dongya Huang⁴, Hedi An⁴, Nan Shen⁴, Yi Wang⁵, Jianqi Li¹, and Guang Yang¹

¹Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, People's Republic of China, ²MR Collaboration NE Asia, Siemens Healthcare, Shanghai, People's Republic of China, ³Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, People's Republic of China, ⁴Department of Neurology, East Hospital Affiliated to Tongji University, Shanghai, People's Republic of China, ⁵Department of Radiology, Weill Medical College of Cornell University, New York, United States

Accurate segmentation of substantia nigra (SN) and red nucleus (RN) in quantitative susceptibility mapping (QSM) images has great clinical value in quantifying iron deposition and measuring disease severity. We propose a new segmentation algorithm which uses the discontinuity of seed points in different tissues as prior knowledge. Seed points in SN or RN can be obtained from standard atlas or specified manually. This prior was then incorporated into level set method to segment SN and RN. Experiments on in-vivo MR images showed that the proposed method achieved more accurate segmentation results than the atlas-based method and classic level-set method.

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