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Applications of deep learning for ophthalmic OCT

Resource type
Thesis type
(Thesis) M.A.Sc.
Date created
2022-01-28
Authors/Contributors
Author: Yu, Timothy
Abstract
Optical coherence tomography (OCT) allows for the cross-sectional visualization of the retinal microvasculature and may benefit clinicians in their management of retinal diseases, such as diabetic retinopathy (DR). However, for DR management, this modality is limited by the field of view (FOV) and patient throughput. This thesis presents machine learning methods to aid in the acceptance of OCT as a secondary modality for DR screening and treatment to improve patient outcomes. First, an increase in FOV reduces the axial resolution. Lower axial resolution was simulated, and a super-resolution generative adversarial network successfully reconstructed lost features. Next, lower lateral resolution was simulated, and the results suggest OCT en face scans may be acquired with 3× fewer lateral scans without affecting a neural network's classification. Finally, machine learning was employed to provide clinicians with an automated classification of DR severity, and federated learning was leveraged to train a more generalizable neural network.
Document
Extent
86 pages.
Identifier
etd21807
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Sarunic, Marinko
Language
English
Member of collection
Download file Size
etd21807.pdf 10.55 MB

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