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.
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.
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