Deep Learning for Domain-Invariant Magnetic Resonance Carotid Artery Wall Segmentation

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dc.contributor.advisorFrayne, Richard
dc.contributor.authorDanko, Anna M.
dc.contributor.committeememberSouza, Roberto Medeiros De
dc.contributor.committeememberRittner, Leticia
dc.contributor.committeememberZhang, Yunyan
dc.date2019-06
dc.date.accessioned2019-04-29T17:23:23Z
dc.date.available2019-04-29T17:23:23Z
dc.date.issued2019-04-26
dc.description.abstractSegmentation of the carotid arteries is a prerequisite to image processing techniques that are applied to medical images to assess the features of atherosclerosis, a disease which can lead to ischemic stroke. Carotid artery segmentation is currently mainly done manually in a time-consuming processing. In this work deep learning approaches were applied to carotid artery segmentation. Additionally, the influence of image contrast on segmentation performance was explored, and whether a network could be taught to learn domain-invariant features including the use of adversarial methods. Non-adversarial and adversarial methods were successfully demonstrated.en_US
dc.identifier.citationDanko, A. M. (2019). Deep Learning for Domain-Invariant Magnetic Resonance Carotid Artery Wall Segmentation (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/36412
dc.identifier.urihttp://hdl.handle.net/1880/110230
dc.language.isoengen_US
dc.publisher.facultyCumming School of Medicineen_US
dc.publisher.institutionUniversity of Calgaryen
dc.rightsUniversity 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.subjectdeep learningen_US
dc.subjectmagnetic resonance imagingen_US
dc.subjectmrien_US
dc.subjectmachine learningen_US
dc.subjectcarotid artery atherosclerosisen_US
dc.subjectstrokeen_US
dc.subjectimage analysisen_US
dc.subjectmedical imagingen_US
dc.subjectcarotid arteriesen_US
dc.subjectsegmentationen_US
dc.subjectvascular imagingen_US
dc.subjectconvolutional neural networken_US
dc.subjectU-Neten_US
dc.subjectdomain shiften_US
dc.subjectmulti-contrast imagingen_US
dc.subject.classificationBiophysics--Medicalen_US
dc.subject.classificationRadiologyen_US
dc.subject.classificationArtificial Intelligenceen_US
dc.subject.classificationEngineering--Biomedicalen_US
dc.titleDeep Learning for Domain-Invariant Magnetic Resonance Carotid Artery Wall Segmentationen_US
dc.typemaster thesisen_US
thesis.degree.disciplineMedicine – Medical Sciencesen_US
thesis.degree.grantorUniversity of Calgaryen_US
thesis.degree.nameMaster of Science (MSc)en_US
ucalgary.item.requestcopytrue
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