Acute Ischemic Stroke Analysis Using Deep Learning-based Image-to-image Translation

Gutierrez Munoz, Jose Alejandro

Acute Ischemic Stroke Analysis Using Deep Learning-based Image-to-image Translation

dc.contributor.advisor	Forkert, Nils Daniel
dc.contributor.author	Gutierrez Munoz, Jose Alejandro
dc.contributor.committeemember	Pike, G. Bruce
dc.contributor.committeemember	Lee, Joon
dc.contributor.committeemember	LeVan, Pierre
dc.contributor.committeemember	Almekhlafi, Mohammed
dc.date	2023-11
dc.date.accessioned	2023-08-09T16:46:30Z
dc.date.available	2023-08-09T16:46:30Z
dc.date.issued	2023-08
dc.description.abstract	Acute ischemic stroke occurs due to the sudden occlusion of a cerebral artery, leading to a disruption in metabolic homeostasis and cell damage. Accurate diagnosis and informed treatment decision-making rely on clinical assessments accompanied by medical imaging. Deep learning methods offer the potential to enhance this decision-making by enabling complex pattern recognition. However, they often rely on large amounts of data, which poses a challenge in stroke centers due to the diverse range of imaging modalities employed. Moreover, specialized processing is often required for the meaningful interpretation of valuable imaging methods like perfusion imaging. Recent advancements in deep learning have made it easier to process and analyze perfusion data by predicting the follow-up tissue outcome. However, these models rely on manual binary lesion segmentations as prediction targets, which may hinder interpretability and limit the amount of available data. To address these limitations, the work described in this thesis utilizes a set of deep learning techniques known as image-to-image translation networks in two distinct ways. First, a method was developed to simulate computed tomography datasets based on magnetic resonance imaging scans and vice versa. The results showed that the proposed approach produces realistic outputs, effectively changing the modality while preserving stroke lesions and brain morphology in follow-up scans. This increases the availability of single-modality data and provides an alternative imaging option for follow-up stroke evaluation. Second, a method was developed to predict stroke tissue outcomes from perfusion scans, without relying on manual lesion segmentations and predicting the follow-up image instead. The results show that the proposed method is able to capture the effects of different treatments, highlighting its potential as a tool for treatment guidance or efficacy evaluation. In conclusion, the application of image-to-image generative modelling proves to be valuable for enhancing acute ischemic stroke analysis and care.
dc.identifier.citation	Gutierrez Munoz, J. A. (2023). Acute ischemic stroke analysis using deep learning-based image-to-image translation (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/116841
dc.identifier.uri	https://dx.doi.org/10.11575/PRISM/41683
dc.language.iso	en
dc.publisher.faculty	Graduate Studies
dc.publisher.institution	University of Calgary
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.
dc.subject.classification	Artificial Intelligence
dc.subject.classification	Radiology
dc.subject.classification	Medicine and Surgery
dc.subject.classification	Bioinformatics
dc.subject.classification	Neuroscience
dc.subject.classification	Pathology
dc.title	Acute Ischemic Stroke Analysis Using Deep Learning-based Image-to-image Translation
dc.type	master thesis
thesis.degree.discipline	Engineering – Biomedical
thesis.degree.grantor	University of Calgary
thesis.degree.name	Master of Science (MSc)
ucalgary.thesis.accesssetbystudent	I require a thesis withhold – I need to delay the release of my thesis due to a patent application, and other reasons outlined in the link above. I have/will need to submit a thesis withhold application.