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

Morgan L. Heisler; Julian Lo; Donghuan Lu; Francis Tran; Arman Athwal; Ivana Zadro; Sven Loncaric; Mirza Faisal Beg; Marinko Sarunic

doi:10.1117/12.2547148

9 March 2020 Progress on machine learning based methods for processing and classification of optical coherence tomography angiography (Conference Presentation)

Morgan L. Heisler, Julian Lo, Donghuan Lu, Francis Tran, Arman Athwal, Ivana Zadro, Sven Loncaric, Mirza Faisal Beg, Marinko Sarunic

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Proceedings Volume 11228, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXIV; 112281Z (2020) https://doi.org/10.1117/12.2547148
Event: SPIE BiOS, 2020, San Francisco, California, United States

Abstract

We present updates upon our novel machine-learning methods for the acquisition, processing, and classification of Optical Coherence Tomography Angiography (OCT-A) images. Transitioning from traditional registration methods to machine-learning based methods provided significant reductions in computation time for serial image acquisition and averaging. Through a vessel segmentation network, clinically useful parameters were extracted and then fed to our classification network which was able to classify different diabetic retinopathy severities. The DNN pipeline was also implemented on data acquired with Sensorless Adaptive Optics OCT-A. This work has potential to subsequently reduce clinical overhead and help expedite treatments, resulting in improved patient prognoses.

Conference Presentation

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Morgan L. Heisler, Julian Lo, Donghuan Lu, Francis Tran, Arman Athwal, Ivana Zadro, Sven Loncaric, Mirza Faisal Beg, and Marinko Sarunic "Progress on machine learning based methods for processing and classification of optical coherence tomography angiography (Conference Presentation)", Proc. SPIE 11228, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXIV, 112281Z (9 March 2020); https://doi.org/10.1117/12.2547148

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KEYWORDS

Angiography

Optical coherence tomography

Machine learning

Capillaries

Adaptive optics

Image segmentation

Image resolution

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