Analysis of Deep Domain Adaptation Methods for Brain Magnetic Resonance Image Segmentation
| dc.contributor.advisor | Hemmati, Hadi | |
| dc.contributor.advisor | Souza, Roberto | |
| dc.contributor.author | Saat, Parisa | |
| dc.contributor.committeemember | Gavrilova, Marina | |
| dc.contributor.committeemember | Deshpande, Gouri | |
| dc.date | 2023-02-24 | |
| dc.date.accessioned | 2022-12-21T16:34:51Z | |
| dc.date.available | 2022-12-21T16:34:51Z | |
| dc.date.issued | 2022-12-16 | |
| dc.description.abstract | Accurate brain segmentation is critical for magnetic resonance imaging (MRI) analysis pipelines. Machine-learning-based brain MR image segmentation methods are among the state-of-the-art techniques for this task. Nevertheless, the segmentations produced by machine learning models often degrade in the presence of expected domain shifts between the test and train sets data distributions. These domain shifts are expected due to several factors, such as scanner hardware and software differences, technology updates, MRI acquisition parameters, and differences across the scanned populations. Domain adaptation (DA) methods can make machine learning models more resilient to these domain shifts. In this thesis, I investigated supervised and unsupervised deep domain adaptation methods for brain MRI segmentation. Two scenarios are investigated. In the first scenario, data shifts occur due to hardware and software differences across different MRI scanner vendors (General Electric, Philips, and Siemens). In the second scenario, data shifts occur due to differences in the scanned populations. The source brain MRI data comes from adults, while the target data corresponds to pediatric patients, whose brains are still developing. The main findings of this thesis are that there is not a single DA technique that consistently outperforms others, and hyperparameter tuning and computational times for these methods still need to be addressed before adopting these methods in clinical practice. Another important finding is that the DA techniques worked better for data shifts resulting from hardware and software differences across different MR scanner vendors than data shifts from population differences. The labeled data and source code used in this thesis were made publicly available and serve as a benchmark for evaluating DA methods for brain MRI segmentation. | en_US |
| dc.identifier.citation | Saat, P. (2022). Analysis of deep domain adaptation methods for brain magnetic resonance image segmentation (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1880/115607 | |
| dc.identifier.uri | https://dx.doi.org/10.11575/PRISM/40541 | |
| dc.language.iso | eng | en_US |
| dc.publisher.faculty | Schulich School of Engineering | en_US |
| dc.publisher.institution | University of 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. | en_US |
| dc.subject | deep learning | en_US |
| dc.subject | domain adaptation | en_US |
| dc.subject | magnetic resonance imaging | en_US |
| dc.subject | neuroimaging | en_US |
| dc.subject | segmentation | en_US |
| dc.subject | brain | en_US |
| dc.subject.classification | Artificial Intelligence | en_US |
| dc.subject.classification | Computer Science | en_US |
| dc.subject.classification | Engineering | en_US |
| dc.subject.classification | Engineering--Biomedical | en_US |
| dc.title | Analysis of Deep Domain Adaptation Methods for Brain Magnetic Resonance Image Segmentation | en_US |
| dc.type | master thesis | en_US |
| thesis.degree.discipline | Engineering – Electrical & Computer | en_US |
| thesis.degree.grantor | University of Calgary | en_US |
| thesis.degree.name | Master of Science (MSc) | en_US |
| ucalgary.item.requestcopy | true | en_US |