رسالة جامعية
Deep Learning for Domain-Invariant Magnetic Resonance Carotid Artery Wall Segmentation
| العنوان: | Deep Learning for Domain-Invariant Magnetic Resonance Carotid Artery Wall Segmentation |
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| المؤلفون: | Danko, Anna M. |
| المساهمون: | Frayne, Richard, Souza, Roberto Medeiros De, Rittner, Leticia, Zhang, Yunyan |
| بيانات النشر: | Cumming School of Medicine University of Calgary |
| سنة النشر: | 2019 |
| المجموعة: | PRISM - University of Calgary Digital Repository |
| مصطلحات موضوعية: | 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, Biophysics--Medical, Radiology, Artificial Intelligence, Engineering--Biomedical |
| الوصف: | 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. |
| نوع الوثيقة: | master thesis |
| وصف الملف: | application/pdf |
| اللغة: | English |
| العلاقة: | Danko, A. M. (2019). Deep Learning for Domain-Invariant Magnetic Resonance Carotid Artery Wall Segmentation (Unpublished master's thesis). University of Calgary, Calgary, AB.; http://dx.doi.org/10.11575/PRISM/36412Test; http://hdl.handle.net/1880/110230Test |
| DOI: | 10.11575/PRISM/36412 |
| الإتاحة: | https://doi.org/10.11575/PRISM/36412Test http://hdl.handle.net/1880/110230Test |
| حقوق: | 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. |
| رقم الانضمام: | edsbas.3A64942F |
| قاعدة البيانات: | BASE |
| DOI: | 10.11575/PRISM/36412 |
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