Automatic Classification of Idiopathic Parkinsonian Disease and Progressive Supranuclear Palsy using Multi-Spectral MRI Datasets: A Machine Learning Approach

Talai, Aron Sahand

Automatic Classification of Idiopathic Parkinsonian Disease and Progressive Supranuclear Palsy using Multi-Spectral MRI Datasets: A Machine Learning Approach

dc.contributor.advisor	Forkert, Nils Daniel
dc.contributor.author	Talai, Aron Sahand
dc.contributor.committeemember	Monchi, Oury
dc.contributor.committeemember	Chan, Sonny
dc.date	2018-11
dc.date.accessioned	2018-10-01T16:09:41Z
dc.date.available	2018-10-01T16:09:41Z
dc.date.issued	2018-09-19
dc.description.abstract	Parkinson's disease, which is characterized by a range of motor and non-motor symptoms is categorized into classical Parkinsonian disease (PD) and atypical Parkinsonian syndromes (APS), such as progressive supranuclear palsy Richardson’s syndrome (PSP-RS). The differential diagnosis between PD and PSP-RS is often challenged by similarity of early symptoms, effectively resulting in considerable misclassification rates. The aim of this thesis is to assess the benefits of using biomarkers from multi-modal MRI datasets in the accurate classification of PD vs. PSP-RS. Multi-spectral information form T1-, T2-, and diffusion-weighted (DWI) MRI from 38 healthy controls (HC), 45 PD, and 20 PSP-RS subjects were available for this study. In detail, morphological (category 1), brain iron marker (category 2), and diffusion features (category 3) were employed. In the last category, all feature types were combined (combinational) for the development of a machine learning model. Nested leave-one-out-cross validation was used to evaluate the classification performance in each category followed by a 1000 permutation test to assess classification significance. The results suggest that, the DWI based classifier tied with the combinational approach in terms of overall accuracy. However, in the former, the specificity was lower by 10%. In detail, 4 PSP-RS and 1 PD subjects are incorrectly classified as PD and PSP-RS in the combinational approach resulting in a sensitivity and specificity of 91.67% and 94.12%, respectively. The obtained results indicate that features extracted from T1- and T2-weighted MRI perform worst based on overall accuracy. All classification categories were statistically significant (p<0.001). In conclusion, combination of features from different MRI modalities such as T1-, T2-, and diffusion-weighted datasets improves the multi-level classification performance of HC vs. PD vs.PSP-RS compared to single modality features, particularly in terms of PD vs. other differentiation. The results and concepts discussed in this research thesis have wide ranging implication for future developments of computer-aided diagnosis of PD sub-syndromes.	en_US
dc.identifier.citation	Talai, A. S. (2018). Automatic Classification of Idiopathic Parkinsonian Disease and Progressive Supranuclear Palsy using Multi-Spectral MRI Datasets: A Machine Learning Approach (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/33060	en_US
dc.identifier.doi	http://dx.doi.org/10.11575/PRISM/33060
dc.identifier.uri	http://hdl.handle.net/1880/108707
dc.language.iso	eng
dc.publisher.faculty	Graduate Studies
dc.publisher.faculty	Schulich School of Engineering
dc.publisher.institution	University of Calgary	en
dc.publisher.place	Calgary	en
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subject	Parkinson's Disease
dc.subject	Progressive Supranuclear Palsy
dc.subject	Machine Learning
dc.subject	Multi-Modal MRI
dc.subject.classification	Education--Sciences	en_US
dc.subject.classification	Radiology	en_US
dc.subject.classification	Artificial Intelligence	en_US
dc.subject.classification	Computer Science	en_US
dc.subject.classification	Engineering--Biomedical	en_US
dc.subject.classification	Psychology--Clinical	en_US
dc.title	Automatic Classification of Idiopathic Parkinsonian Disease and Progressive Supranuclear Palsy using Multi-Spectral MRI Datasets: A Machine Learning Approach
dc.type	master thesis
thesis.degree.discipline	Biomedical Engineering
thesis.degree.grantor	University of Calgary
thesis.degree.name	Master of Science (MSc)
ucalgary.item.requestcopy	true