Deep Learning for Domain-Invariant Magnetic Resonance Carotid Artery Wall Segmentation
Date
2019-04-26
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Abstract
Segmentation 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.
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Keywords
deep learning, magnetic resonance imaging, mri, machine learning, carotid artery atherosclerosis, stroke, image analysis, medical imaging, carotid arteries, segmentation, vascular imaging, convolutional neural network, U-Net, domain shift, multi-contrast imaging
Citation
Danko, 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.