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.
High quality visualization of the retinal microvasculature can improve our understanding of the onset and development of retinal vascular diseases, especially Diabetic Retinopathy (DR), which is a major cause of visual morbidity and is increasing in prevalence. Optical Coherence Tomography Angiography (OCT-A) images are acquired over multiple seconds and are particularly susceptible to motion artifacts, which are more prevalent when imaging individuals with DR whose ability to fixate is limited due to deteriorating vision. The sequential acquisition and averaging of multiple OCT-A images can be performed for removing motion artifact and increasing the contrast of the vascular network. As motion artifacts often irreversibly corrupt OCT-A images of DR eyes, a robust registration pipeline is needed before feature preserving image averaging can be performed.
In this report we present an improvement upon a novel method for the acquisition, processing, segmentation, registration, and averaging of sequentially acquired OCT-A images, to correct for motion artifacts in images of DR eyes. Image discontinuities caused by rapid micro-saccadic movements and image warping due to smoother reflex movements were corrected by strip-wise affine registration and subsequent local similarity-based non-rigid registration. Where our previous work was limited by the need for at least one image containing no motion artifact, thus reducing its clinical relevance, this novel template-less method stitches together partial images to form complete, motion-free images. These techniques significantly improve image quality, increasing the value for clinical diagnosis and increasing the range of patients for whom high quality OCT-A images can be acquired.
We present a multiscale sensorless adaptive optics (SAO) OCT system capable of imaging retinal structure and vasculature with various fields-of-view (FOV) and resolutions. Using a single deformable mirror and exploiting the polarization properties of light, the SAO-OCT-A was implemented in a compact and easy to operate system. With the ability to adjust the beam diameter at the pupil, retinal imaging was demonstrated at two different numerical apertures with the same system. The general morphological structure and retinal vasculature could be observed with a few tens of micrometer-scale lateral resolution with conventional OCT and OCT-A scanning protocols with a 1.7-mm-diameter beam incident at the pupil and a large FOV (15 deg× 15 deg). Changing the system to a higher numerical aperture with a 5.0-mm-diameter beam incident at the pupil and the SAO aberration correction, the FOV was reduced to 3 deg× 3 deg for fine detailed imaging of morphological structure and microvasculature such as the photoreceptor mosaic and capillaries. Multiscale functional SAO-OCT imaging was performed on four healthy subjects, demonstrating its functionality and potential for clinical utility.
Adaptive optics optical coherence tomography (AO-OCT) systems capable of 3D high resolution imaging have been applied to posterior eye imaging in order to resolve the fine morphological features in the retina. Human cone photoreceptors have been extensively imaged and studied for the investigation of retinal degeneration resulting in photoreceptor cell death. However, there are still limitations of conventional approaches to AO in the clinic, such as relatively small field-of-view (FOV) and the complexities in system design and operation.
In this research, a recently developed phase-resolved Sensorless AO Swept Source based OCT (SAO-SS-OCT) system which is compact in size and easy to operate is presented. Owing to its lens-based system design, wide-field imaging can be performed up to 6° on the retina. A phase stabilization unit was integrated with the OCT system. With the phase stabilized OCT signal, we constructed retinal micro-vasculature image using a phase variance technique. The retinal vasculature image was used to align and average multiple OCT volumes acquired sequentially. The contrast-enhanced photoreceptor projection image was then extracted from the averaged volume, and analyzed based on its morphological features through a novel photoreceptor structure evaluation algorithm.
The retinas of twelve human research subjects (10 normal and 2 pathological cases) were measured in vivo. Quantitative parameters used for evaluating the cone photoreceptor mosaic such as cell density, cell area, and mosaic regularity are presented and discussed. The SAO-SS-OCT system and the proposed photoreceptor evaluation method has significant potential to reveal early stage retinal diseases associated with retinal degeneration.
Optical Coherence Tomography (OCT) has revolutionized modern ophthalmology, providing depth resolved images of the retinal layers in a system that is suited to a clinical environment. A limitation of the performance and utilization of the OCT systems has been the lateral resolution. Through the combination of wavefront sensorless adaptive optics with dual variable optical elements, we present a compact lens based OCT system that is capable of imaging the photoreceptor mosaic. We utilized a commercially available variable focal length lens to correct for a wide range of defocus commonly found in patient eyes, and a multi-actuator adaptive lens after linearization of the hysteresis in the piezoelectric actuators for aberration correction to obtain near diffraction limited imaging at the retina. A parallel processing computational platform permitted real-time image acquisition and display. The Data-based Online Nonlinear Extremum seeker (DONE) algorithm was used for real time optimization of the wavefront sensorless adaptive optics OCT, and the performance was compared with a coordinate search algorithm. Cross sectional images of the retinal layers and en face images of the cone photoreceptor mosaic acquired in vivo from research volunteers before and after WSAO optimization are presented. Applying the DONE algorithm in vivo for wavefront sensorless AO-OCT demonstrates that the DONE algorithm succeeds in drastically improving the signal while achieving a computational time of 1 ms per iteration, making it applicable for high speed real time applications.
Adaptive optics has been successfully applied to cellular resolution imaging of the retina, enabling visualization of the characteristic mosaic patterns of the outer retina. Wavefront sensorless adaptive optics (WSAO) is a novel technique that facilitates high resolution ophthalmic imaging; it replaces the Hartmann-Shack Wavefront Sensor with an image-driven optimization algorithm and mitigates some the challenges encountered with sensor-based designs. However, WSAO generally requires longer time to perform aberrations correction than the conventional closed-loop adaptive optics. When used for in vivo retinal imaging applications, motion artifacts during the WSAO optimization process will affect the quality of the aberration correction. A faster converging optimization scheme needs to be developed to account for rapid temporal variation of the wavefront and continuously apply corrections. In this project, we investigate the Databased Online Nonlinear Extremum-seeker (DONE), a novel non-linear multivariate optimization algorithm in combination with in vivo human WSAO OCT imaging. We also report both hardware and software updates of our compact lens based WSAO 1060nm swept source OCT human retinal imaging system, including real time retinal layer segmentation and tracking (ILM and RPE), hysteresis correction for the multi-actuator adaptive lens, precise synchronization control for the 200kHz laser source, and a zoom lens unit for rapid switching of the field of view. Cross sectional images of the retinal layers and en face images of the cone photoreceptor mosaic acquired in vivo from research volunteers before and after WSAO optimization are presented.
High quality visualization of the retinal microvasculature can improve our understanding of the onset and development of retinal vascular diseases, which are a major cause of visual morbidity and are increasing in prevalence. Optical Coherence Tomography Angiography (OCT-A) images are acquired over multiple seconds and are particularly susceptible to motion artifacts, which are more prevalent when imaging patients with pathology whose ability to fixate is limited. The acquisition of multiple OCT-A images sequentially can be performed for the purpose of removing motion artifact and increasing the contrast of the vascular network through averaging. Due to the motion artifacts, a robust registration pipeline is needed before feature preserving image averaging can be performed.
In this report, we present a novel method for a GPU-accelerated pipeline for acquisition, processing, segmentation, and registration of multiple, sequentially acquired OCT-A images to correct for the motion artifacts in individual images for the purpose of averaging. High performance computing, blending CPU and GPU, was introduced to accelerate processing in order to provide high quality visualization of the retinal microvasculature and to enable a more accurate quantitative analysis in a clinically useful time frame. Specifically, image discontinuities caused by rapid micro-saccadic movements and image warping due to smoother reflex movements were corrected by strip-wise affine registration estimated using Scale Invariant Feature Transform (SIFT) keypoints and subsequent local similarity-based non-rigid registration. These techniques improve the image quality, increasing the value for clinical diagnosis and increasing the range of patients for whom high quality OCT-A images can be acquired.
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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.