A Fully-Automatic Framework for Parkinson's Disease Diagnosis by Multi-Modality Images
Background: Parkinson's disease (PD) is a prevalent long-term neurodegenerative disease. Though the diagnostic criteria of PD are relatively well defined, the current medical imaging diagnostic procedures are expertise-demanding, and thus call for a higher-integrated AI-based diagnostic algorithm. Methods: In this paper, we proposed an automatic, end-to-end,
multi-modality diagnosis framework, including segmentation, registration, feature generation and machine learning, to process the information of the striatum for the diagnosis of PD. Multiple modalities, including T1- weighted MRI and 11C-CFT PET, were used in the proposed framework. The reliability of this framework was then validated on a dataset from the PET center of Huashan Hospital, as the dataset contains paired T1-MRI and CFT-PET images of 18 Normal (NL) subjects and 49 PD subjects. Results: We obtained an accuracy of 100% for the PD/NL classification task, besides, we conducted several comparative experiments to validate the diagnosis ability of our framework. Conclusion: Through experiment we illustrate that (1) automatic segmentation has the same classification effect as the manual segmentation, (2) the multi-modality images generates a better prediction than single modality images, and (3) volume feature is shown to be irrelevant to PD diagnosis.
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Podrobná bibliografie
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Publikováno v
- arXiv.org
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Hlavní autoři
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Xu, Jiahang,
Jiao, Fangyang,
Huang, Yechong,
Luo, Xinzhe,
Xu, Qian,
Li, Ling,
Liu, Xueling,
Zuo, Chuantao,
Wu, Ping,
Zhuang, Xiahai
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Typ dokumentu
- Paper
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Jazyk dokumentu
- English
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Vydáno
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Ithaca
Cornell University Library, arXiv.org
26. 02. 2019
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Témata
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Bibliografie
- content type line 50
SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
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ISSN
- 2331-8422
