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

ABSTRACT:

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) subject

SUBMITTER: Xu J 

PROVIDER: S-EPMC6716425 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature