Deep learning for optical coherence tomography angiography: Quantifying microvascular changes in diabetic retinopathy

Author: 
Date created: 
2020-12-18
Identifier: 
etd21225
Keywords: 
Optical coherence tomography
Angiography
Image processing
Machine learning
Deep neural networks
Federated learning
Abstract: 

Optical Coherence Tomography Angiography (OCT-A) permits visualization of the changes to the retinal circulation due to diabetic retinopathy (DR), a microvascular complication of diabetes. Machine learning applications have directly benefited ophthalmology, leveraging large amounts of data to create frameworks to aid clinical decision-making. In this thesis, several techniques to quantify the retinal microvasculature are explored. First, high-quality, averaged, 6x6mm OCT-A enface images are used to produce manual segmentations for the corresponding lower-quality, single-frame images to produce more training data. Using transfer learning, the resulting convolutional neural network (CNN) segmented the superficial capillary plexus and deep vascular complex with performance exceeding inter-rater comparisons. Next, a federated learning framework was designed to allow for collaborative training by multiple participants on a de-centralized data corpus. When trained for microvasculature segmentation, the framework achieved comparable performance to a CNN trained on a fully-centralized dataset.

Document type: 
Thesis
Rights: 
This thesis may be printed or downloaded for non-commercial research and scholarly purposes. Copyright remains with the author.
File(s): 
Supervisor(s): 
Marinko Sarunic
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) M.A.Sc.
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