Progress on machine learning based methods for processing and classification of optical coherence tomography angiography (Conference Presentation)

2020 ◽  
Author(s):  
Morgan L. Heisler ◽  
Julian Lo ◽  
Donghuan Lu ◽  
Francis Tran ◽  
Arman Athwal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Coherence Tomography ◽  
Optical Coherence Tomography Angiography ◽  
Optical Coherence

scholarly journals Machine learning in optical coherence tomography angiography

2021 ◽  
pp. 153537022110265
Author(s):  
David Le ◽  
Taeyoon Son ◽  
Xincheng Yao
Keyword(s):  
Machine Learning ◽  
Optical Coherence Tomography ◽  
Image Analysis ◽  
Human Error ◽  
Optical Coherence Tomography Angiography ◽  
Disease Classification ◽  
Optical Coherence ◽  
Label Free ◽  
Machine Learning Classification ◽  
Recent Developments

Optical coherence tomography angiography (OCTA) offers a noninvasive label-free solution for imaging retinal vasculatures at the capillary level resolution. In principle, improved resolution implies a better chance to reveal subtle microvascular distortions associated with eye diseases that are asymptomatic in early stages. However, massive screening requires experienced clinicians to manually examine retinal images, which may result in human error and hinder objective screening. Recently, quantitative OCTA features have been developed to standardize and document retinal vascular changes. The feasibility of using quantitative OCTA features for machine learning classification of different retinopathies has been demonstrated. Deep learning-based applications have also been explored for automatic OCTA image analysis and disease classification. In this article, we summarize recent developments of quantitative OCTA features, machine learning image analysis, and classification.

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