Analysis of Deep Domain Adaptation Methods for Brain Magnetic Resonance Image Segmentation

Saat, Parisa

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