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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Neurosci. | doi: 10.3389/fnins.2019.00874

A Fully-Automatic Framework for Parkinson’s Disease Diagnosis by Multi-Modality Images

  • 1School of Data Science, Fudan University, China
  • 2Department of Nuclear Medicine, Chinese PLA General Hospital, China
  • 3Department of Nuclear Medicine, North Huashan Hospital, Fudan University, China
  • 4PET Center, Huashan Hospital, Fudan University, China
  • 5Department of Radiology, Huashan Hospital, Fudan University, China

Background: Parkinson's disease (PD) is a prevalent long-term neurodegenerative disease. Though the criteria of PD diagnosis are relatively well defined, current diagnostic procedures using medical images are labor-intensive and expertise-demanding. Hence, highly integrated automatic diagnostic algorithms are desirable.
Methods: In this work, we propose an end-to-end multi-modality diagnostic framework, including segmentation, registration, feature extraction and machine learning, to analyze the features of striatum for PD diagnosis. Multi-modality images, including T1-weighted MRI and 11C-CFT PET, are integrated into the proposed framework. The reliability of this method is validated on a dataset with the paired images from 49 PD subjects and 18 Normal (NL) subjects.
Results: We obtained a promising diagnostic accuracy in the PD/NL classification task. Meanwhile, several comparative experiments were conducted to validate the performance of the proposed framework.
Conclusion: We demonstrated that (1) the automatic segmentation provides accurate results for the diagnostic framework, (2) the method combining multi-modality images generates a better prediction accuracy than the method with single-modality PET images, and (3) the volume of the striatum is proved to be irrelevant to PD diagnosis.

Keywords: Parkinsion's disease (PD), multi-modality, Image clarification, U-net, Striatum

Received: 14 Feb 2019; Accepted: 05 Aug 2019.

Edited by:

Siyang Zuo, Tianjin University, China

Reviewed by:

Delia Cabrera DeBuc, University of Miami, United States
Danny J. Wang, University of Southern California, United States  

Copyright: © 2019 Xu, Jiao, Huang, Luo, Xu, LI, Liu, Zuo, Wu and Zhuang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
MD. Ping Wu, PET Center, Huashan Hospital, Fudan University, Shanghai, China, wupingpet@fudan.edu.cn
Dr. Xiahai Zhuang, School of Data Science, Fudan University, Shanghai, 200433, Shanghai Municipality, China, zxh@fudan.edu.cn