Machine learning for optical coherence tomography angiography

Julian Lo; Morgan Heisler; Arman Athwal; Francis Tran; Marinko V. Sarunic

doi:10.1117/12.2527210

19 July 2019 Machine learning for optical coherence tomography angiography

Julian Lo, Morgan Heisler, Arman Athwal, Francis Tran, Marinko V. Sarunic

Proceedings Volume 11078, Optical Coherence Imaging Techniques and Imaging in Scattering Media III; 110781I (2019) https://doi.org/10.1117/12.2527210
Event: European Conferences on Biomedical Optics, 2019, Munich, Germany

Abstract

Optical coherence tomography angiography (OCT-A) is a non-invasive imaging modality allowing researchers and clinicians to view the retina in micrometer-scale detail. Acquired OCT-A volumes are three-dimensional, allowing the visualization of the superficial capillary plexus (SCP) and the deep capillary plexus (DCP). This provides valuable information towards the identification of pathologies such as diabetic retinopathy (DR). However, because an OCT-A volume is acquired over several seconds, motion artifacts caused by rapid movements of the subject’s eye (also known as micro-saccadic motion) can greatly reduce the quality, and subsequently the clinical utility, of the resulting volumes. Hardware motion tracking aims to reduce the effect of motion, but non-rigid registration is still often required for averaging sequentially acquired images. Furthermore, not all prototype OCT-A systems have tracking capabilities, particularly adaptive optics (AO) systems. Because of this, image registration is essential for the elimination of motion artifacts in OCT-A volumes, increasing their clinical diagnostic value. To further improve the clinical utility of these OCT-A images, segmentation is essential as it allows for the quantitative analysis of the microvasculature, which include the identification of the foveal avascular zone (FAZ) and areas of capillary non-perfusion (CNP), two biomarkers for the progression of DR.

Citation Download Citation

Julian Lo, Morgan Heisler, Arman Athwal, Francis Tran, and Marinko V. Sarunic "Machine learning for optical coherence tomography angiography", Proc. SPIE 11078, Optical Coherence Imaging Techniques and Imaging in Scattering Media III, 110781I (19 July 2019); https://doi.org/10.1117/12.2527210

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