Frayne, RichardDanko, Anna M.2019-04-292019-04-292019-04-26Danko, 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.http://hdl.handle.net/1880/110230Segmentation 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.engUniversity 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.deep learningmagnetic resonance imagingmrimachine learningcarotid artery atherosclerosisstrokeimage analysismedical imagingcarotid arteriessegmentationvascular imagingconvolutional neural networkU-Netdomain shiftmulti-contrast imagingBiophysics--MedicalRadiologyArtificial IntelligenceEngineering--Biomedicalmaster thesis10.11575/PRISM/36412