The visibility of retinal microvasculature in optical coherence tomography angiography (OCT-A) images is negatively affected by the small dimension of the capillaries, pulsatile blood flow, and motion artifacts. Serial acquisition and time-averaging of multiple OCT-A images can enhance the definition of the capillaries and result in repeatable and consistent visualization. We demonstrate an automated method for registration and averaging of serially acquired OCT-A images. Ten OCT-A volumes from six normal control subjects were acquired using our prototype 1060-nm swept source OCT system. The volumes were divided into microsaccade-free en face angiogram strips, which were affine registered using scale-invariant feature transform keypoints, followed by nonrigid registration by pixel-wise local neighborhood matching. The resulting averaged images were presented of all the retinal layers combined, as well as in the superficial and deep plexus layers separately. The contrast-to-noise ratio and signal-to-noise ratio of the angiograms with all retinal layers (reported as average±standard deviation) increased from 0.52±0.22 and 19.58±4.04 dB for a single image to 0.77±0.25 and 25.05±4.73 dB, respectively, for the serially acquired images after registration and averaging. The improved visualization of the capillaries can enable robust quantification and study of minute changes in retinal microvasculature.
Accurate segmentation of the retinal microvasculature is a critical step in the quantitative analysis of the retinal circulation, which can be an important marker in evaluating the severity of retinal diseases. As manual segmentation remains the gold standard for segmentation of optical coherence tomography angiography (OCT-A) images, we present a method for automating the segmentation of OCT-A images using deep neural networks (DNNs). Eighty OCT-A images of the foveal region in 12 eyes from 6 healthy volunteers were acquired using a prototype OCT-A system and subsequently manually segmented. The automated segmentation of the blood vessels in the OCT-A images was then performed by classifying each pixel into vessel or nonvessel class using deep convolutional neural networks. When the automated results were compared against the manual segmentation results, a maximum mean accuracy of 0.83 was obtained. When the automated results were compared with inter and intrarater accuracies, the automated results were shown to be comparable to the human raters suggesting that segmentation using DNNs is comparable to a second manual rater. As manually segmenting the retinal microvasculature is a tedious task, having a reliable automated output such as automated segmentation by DNNs, is an important step in creating an automated output.
We present a Graph Cut based image segmentation that was implemented on a Graphics Processing Unit for acceleration of processing retinal images acquired with OCT. We applied this work to generate a retinal thickness map, and for retinal layer segmentation to enhance the visualization of vasculature networks from distinct retinal capillary beds during acquisition using speckle variance OCT.