Acner Camino

Evaluation of artifact reduction in optical coherence tomography angiography with real-time tracking and motion correction technology

Artifacts introduced by eye motion in optical coherence tomography angiography (OCTA) affect the interpretation of images and the quantification of parameters with clinical value. Eradication of such artifacts in OCTA remains a technical challenge. We developed an algorithm that recognizes five different types of motion artifacts and used it to evaluate the performance of three motion removal technologies. On en face maximum projection of flow images, the summed flow signal in each row and column and the correlation between neighboring rows and columns were calculated. Bright line artifacts were recognized by large summed flow signal. Drifts, distorted lines, and stretch artifacts exhibited abnormal correlation values. Residual lines were simultaneously a local maximum of summed flow and a local minimum of correlation. Tracking-assisted scanning integrated with motion correction technology (MCT) demonstrated higher performance than tracking or MCT alone in healthy and diabetic eyes.

Automated detection of preserved photoreceptor on optical coherence tomography in choroideremia based on machine learning

Optical coherence tomography (OCT) can demonstrate early deterioration of the photoreceptor integrity caused by inherited retinal degeneration diseases (IRDs). A machine learning method based on random forests was developed to automatically detect continuous areas of preserved ellipsoid zone structure (an easily recognizable part of the photoreceptors on OCT) in 16 eyes of patients with choroideremia (a type of IRD). Pseudopodial extensions protruding from the preserved ellipsoid zone areas are detected separately by a local active contour routine. The algorithm is implemented on en face images with minimum segmentation requirements, only needing delineation of the Bruchs membrane, thus evading the inaccuracies and technical challenges associated with automatic segmentation of the ellipsoid zone in eyes with severe retinal degeneration.

Regression-based algorithm for bulk motion subtraction in optical coherence tomography angiography

We developed an algorithm to remove decorrelation noise due to bulk motion in optical coherence tomography angiography (OCTA) of the posterior eye. In this algorithm, OCTA B-frames were divided into segments within which the bulk motion velocity could be assumed to be constant. This velocity was recovered using linear regression of decorrelation versus the logarithm of reflectance in axial lines (A-lines) identified as bulk tissue by percentile analysis. The fitting parameters were used to calculate a reflectance-adjusted upper bound threshold for bulk motion decorrelation. Below this threshold, voxels are identified as non-flow tissue, their flow values are set to zeros. Above this threshold, the voxels are identified as flow voxels and bulk motion velocity is subtracted from each using a nonlinear decorrelation-velocity relationship previously established in laboratory flow phantoms. Compared to the simpler median-subtraction method, the regression-based bulk motion subtraction improved angiogram signal-to-noise ratio, contrast, vessel density repeatability, and bulk motion noise cleanup in the foveal avascular zone, while preserving the connectivity of the vascular networks in the angiogram.

Deep learning for the segmentation of preserved photoreceptors on en face optical coherence tomography in two inherited retinal diseases

The objective quantification of photoreceptor loss in inherited retinal degenerations (IRD) is essential for measuring disease progression, and is now especially important with the growing number of clinical trials. Optical coherence tomography (OCT) is a non-invasive imaging technology widely used to recognize and quantify such anomalies. Here, we implement a versatile method based on a convolutional neural network to segment the regions of preserved photoreceptors in two different IRDs (choroideremia and retinitis pigmentosa) from OCT images. An excellent segmentation accuracy (~90%) was achieved for both IRDs. Due to the flexibility of this technique, it has potential to be extended to additional IRDs in the future.

Automatic quantification of choroidal neovascularization lesion area on OCT angiography based on density cell-like P systems with active membranes

Detecting and quantifying the size of choroidal neovascularization (CNV) is important for the diagnosis and assessment of neovascular age-related macular degeneration. Depth-resolved imaging of the retinal and choroidal vasculature by optical coherence tomography angiography (OCTA) has enabled the visualization of CNV. However, due to the prevalence of artifacts, it is difficult to segment and quantify the CNV lesion area automatically. We have previously described a saliency algorithm for CNV detection that could identify a CNV lesion area with 83% accuracy. However, this method works under the assumption that the CNV region is the most salient area for visual attention in the whole image and consequently, errors occur when this requirement is not met (e.g. when the lesion occupies a large portion of the image). Moreover, saliency image processing methods cannot extract the edges of the salient object very accurately. In this paper, we propose a novel and automatic CNV segmentation method based on an unsupervised and parallel machine learning technique named density cell-like P systems (DEC P systems). DEC P systems integrate the idea of a modified clustering algorithm into cell-like P systems. This method improved the accuracy of detection to 87.2% on 22 subjects and obtained clear boundaries of the CNV lesions.

Automated detection of photoreceptor disruption in mild diabetic retinopathy on volumetric optical coherence tomography

Diabetic retinopathy is a pathology where microvascular circulation abnormalities ultimately result in photoreceptor disruption and, consequently, permanent loss of vision. Here, we developed a method that automatically detects photoreceptor disruption in mild diabetic retinopathy by mapping ellipsoid zone reflectance abnormalities from en face optical coherence tomography images. The algorithm uses a fuzzy c-means scheme with a redefined membership function to assign a defect severity level on each pixel and generate a probability map of defect category affiliation. A novel scheme of unsupervised clustering optimization allows accurate detection of the affected area. The achieved accuracy, sensitivity and specificity were about 90% on a population of thirteen diseased subjects. This method shows potential for accurate and fast detection of early biomarkers in diabetic retinopathy evolution.

Fast and robust standard-deviation-based method for bulk motion compensation in phase-based functional OCT

Phase-based optical coherence tomography (OCT), such as OCT angiography (OCTA) and Doppler OCT, is sensitive to the confounding phase shift introduced by subject bulk motion. Traditional bulk motion compensation methods are limited by their accuracy and computing cost-effectiveness. In this Letter, to the best of our knowledge, we present a novel bulk motion compensation method for phase-based functional OCT. Bulk motion associated phase shift can be directly derived by solving its equation using a standard deviation of phase-based OCTA and Doppler OCT flow signals. This method was evaluated on rodent retinal images acquired by a prototype visible light OCT and human retinal images acquired by a commercial system. The image quality and computational speed were significantly improved, compared to two conventional phase compensation methods.

MEDnet, a neural network for automated detection of avascular area in OCT angiography

Screening and assessing diabetic retinopathy (DR) are essential for reducing morbidity associated with diabetes. Macular ischemia is known to correlate with the severity of retinopathy. Recent studies have shown that optical coherence tomography angiography (OCTA), with intrinsic contrast from blood flow motion, is well suited for quantified analysis of the avascular area, which is potentially a useful biomarker in DR. In this study, we propose the first deep learning solution to segment the avascular area in OCTA of DR. The network design consists of a multi-scaled encoder-decoder neural network (MEDnet) to detect the non-perfusion area in 6 × 6 mm2 and in ultra-wide field retinal angiograms. Avascular areas were effectively detected in DR subjects of various disease stages as well as in the foveal avascular zone of healthy subjects.

Automated drusen detection in dry age-related macular degeneration by multiple-depth, en face optical coherence tomography

We introduce a method to automatically detect drusen in dry age-related macular degeneration (AMD) from optical coherence tomography with minimum need for layer segmentation. The method is based on the en face detection of drusen areas in C-scans at certain distances above the Bruchs membrane, circumventing the difficult task of pathologic retinal pigment epithelium segmentation. All types of drusen can be detected, including the challenging subretinal drusenoid deposits (pseudodrusen). The high sensitivity and accuracy demonstrated here shows its potential for detection of drusen onset in early AMD.

Angiographic and structural imaging using high axial resolution fiber-based visible-light OCT

Optical coherence tomography using visible-light sources can increase the axial resolution without the need for broader spectral bandwidth. Here, a high-resolution, fiber-based, visible-light optical coherence tomography system is built and used to image normal retina in rats and blood vessels in chicken embryo. In the rat retina, accurate segmentation of retinal layer boundaries and quantification of layer thicknesses are accomplished. Furthermore, three distinct capillary plexuses in the retina and the choriocapillaris are identified and the characteristic pattern of the nerve fiber layer thickness in rats is revealed. In the chicken embryo model, the microvascular network and a venous bifurcation are examined and the ability to identify and segment large vessel walls is demonstrated.