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Proceedings Paper

Progress on machine learning based methods for processing and classification of optical coherence tomography angiography (Conference Presentation)
Author(s): Morgan L. Heisler; Julian Lo; Donghuan Lu; Francis Tran; Arman Athwal; Ivana Zadro; Sven Loncaric; Mirza Faisal Beg; Marinko Sarunic

Paper 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.

Paper Details

Date Published: 9 March 2020
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Proc. SPIE 11228, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXIV, 112281Z (9 March 2020); doi: 10.1117/12.2547148
Show Author Affiliations
Morgan L. Heisler, Simon Fraser Univ. (Canada)
Julian Lo, Simon Fraser Univ. (Canada)
Donghuan Lu, Simon Fraser Univ. (Canada)
Francis Tran, Simon Fraser Univ. (Canada)
Arman Athwal, Simon Fraser Univ. (Canada)
Ivana Zadro, Univ. of Zagreb (Croatia)
Sven Loncaric, Univ. of Zagreb (Croatia)
Mirza Faisal Beg, Simon Fraser Univ. (Canada)
Marinko Sarunic, Simon Fraser Univ. (Canada)


Published in SPIE Proceedings Vol. 11228:
Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXIV
Joseph A. Izatt; James G. Fujimoto, Editor(s)

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