Meeting Banner
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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here