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 |