A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head

Purpose: To develop a deep learning approach to de-noise optical coherence tomography (OCT) B-scans of the optic nerve head (ONH). Methods: Volume scans consisting of 97 horizontal B-scans were acquired through the center of the ONH using a commercial OCT device (Spectralis) for both eyes of 20 subjects. For each eye, single-frame (without signal averaging), and multi-frame (75x signal averaging) volume scans were obtained. A custom deep learning network was then designed and trained with 2,328 "clean B-scans" (multi-frame B-scans), and their corresponding "noisy B-scans" (clean B-scans + gaussian noise) to de-noise the single-frame B-scans. The performance of the de-noising algorithm was assessed qualitatively, and quantitatively on 1,552 B-scans using the signal to noise ratio (SNR), contrast to noise ratio (CNR), and mean structural similarity index metrics (MSSIM). Results: The proposed algorithm successfully denoised unseen single-frame OCT B-scans. The denoised B-scans were qualitatively similar to their corresponding multi-frame B-scans, with enhanced visibility of the ONH tissues. The mean SNR increased from $4.02 \pm 0.68$ dB (single-frame) to $8.14 \pm 1.03$ dB (denoised). For all the ONH tissues, the mean CNR increased from $3.50 \pm 0.56$ (single-frame) to $7.63 \pm 1.81$ (denoised). The MSSIM increased from $0.13 \pm 0.02$ (single frame) to $0.65 \pm 0.03$ (denoised) when compared with the corresponding multi-frame B-scans. Conclusions: Our deep learning algorithm can denoise a single-frame OCT B-scan of the ONH in under 20 ms, thus offering a framework to obtain superior quality OCT B-scans with reduced scanning times and minimal patient discomfort

Effect of Uveal Melanocytes on Choroidal Morphology in Rhesus Macaques and Humans on Enhanced-Depth Imaging Optical Coherence Tomography.

PurposeTo compare cross-sectional choroidal morphology in rhesus macaque and human eyes using enhanced-depth imaging optical coherence tomography (EDI-OCT) and histologic analysis.MethodsEnhanced-depth imaging-OCT images from 25 rhesus macaque and 30 human eyes were evaluated for choriocapillaris and choroidal-scleral junction (CSJ) visibility in the central macula based on OCT reflectivity profiles, and compared with age-matched histologic sections. Semiautomated segmentation of the choriocapillaris and CSJ was used to measure choriocapillary and choroidal thickness, respectively. Multivariate regression was performed to determine the association of age, refractive error, and race with choriocapillaris and CSJ visibility.ResultsRhesus macaques exhibit a distinct hyporeflective choriocapillaris layer on EDI-OCT, while the CSJ cannot be visualized. In contrast, humans show variable reflectivities of the choriocapillaris, with a distinct CSJ seen in many subjects. Histologic sections demonstrate large, darkly pigmented melanocytes that are densely distributed in the macaque choroid, while melanocytes in humans are smaller, less pigmented, and variably distributed. Optical coherence tomography reflectivity patterns of the choroid appear to correspond to the density, size, and pigmentation of choroidal melanocytes. Mean choriocapillary thickness was similar between the two species (19.3 ± 3.4 vs. 19.8 ± 3.4 μm, P = 0.615), but choroidal thickness may be lower in macaques than in humans (191.2 ± 43.0 vs. 266.8 ± 78.0 μm, P < 0.001). Racial differences in uveal pigmentation also appear to affect the visibility of the choriocapillaris and CSJ on EDI-OCT.ConclusionsPigmented uveal melanocytes affect choroidal morphology on EDI-OCT in rhesus macaque and human eyes. Racial differences in pigmentation may affect choriocapillaris and CSJ visibility, and may influence the accuracy of choroidal thickness measurements

Peripapillary and macular choroidal thickness in glaucoma.

PurposeTo compare choroidal thickness (CT) between individuals with and without glaucomatous damage and to explore the association of peripapillary and submacular CT with glaucoma severity using spectral domain optical coherence tomography (SD-OCT).MethodsNinety-one eyes of 20 normal subjects and 43 glaucoma patients from the UCLA SD-OCT Imaging Study were enrolled. Imaging was performed using Cirrus HD-OCT. Choroidal thickness was measured at four predetermined points in the macular and peripapillary regions, and compared between glaucoma and control groups before and after adjusting for potential confounding variables.ResultsThe average (± standard deviation) mean deviation (MD) on visual fields was -0.3 (±2.0) dB in controls and -3.5 (±3.5) dB in glaucoma patients. Age, axial length and their interaction were the most significant factors affecting CT on multivariate analysis. Adjusted average CT (corrected for age, axial length, their interaction, gender and lens status) however, was not different between glaucoma patients and the control group (P=0.083) except in the temporal parafoveal region (P=0.037); nor was choroidal thickness related to glaucoma severity (r=-0.187, P=0.176 for correlation with MD, r=-0.151, P=0.275 for correlation with average nerve fiber layer thickness).ConclusionsChoroidal thickness of the macular and peripapillary regions is not decreased in glaucoma. Anatomical measurements with SD-OCT do not support the possible influence of the choroid on the pathophysiology of glaucoma

Validation of automated artificial intelligence segmentation of optical coherence tomography images

PURPOSE To benchmark the human and machine performance of spectral-domain (SD) and swept-source (SS) optical coherence tomography (OCT) image segmentation, i.e., pixel-wise classification, for the compartments vitreous, retina, choroid, sclera. METHODS A convolutional neural network (CNN) was trained on OCT B-scan images annotated by a senior ground truth expert retina specialist to segment the posterior eye compartments. Independent benchmark data sets (30 SDOCT and 30 SSOCT) were manually segmented by three classes of graders with varying levels of ophthalmic proficiencies. Nine graders contributed to benchmark an additional 60 images in three consecutive runs. Inter-human and intra-human class agreement was measured and compared to the CNN results. RESULTS The CNN training data consisted of a total of 6210 manually segmented images derived from 2070 B-scans (1046 SDOCT and 1024 SSOCT; 630 C-Scans). The CNN segmentation revealed a high agreement with all grader groups. For all compartments and groups, the mean Intersection over Union (IOU) score of CNN compartmentalization versus group graders' compartmentalization was higher than the mean score for intra-grader group comparison. CONCLUSION The proposed deep learning segmentation algorithm (CNN) for automated eye compartment segmentation in OCT B-scans (SDOCT and SSOCT) is on par with manual segmentations by human graders

3D Automatic Segmentation Method for Retinal Optical Coherence Tomography Volume Data Using Boundary Surface Enhancement

With the introduction of spectral-domain optical coherence tomography (SDOCT), much larger image datasets are routinely acquired compared to what was possible using the previous generation of time-domain OCT. Thus, there is a critical need for the development of 3D segmentation methods for processing these data. We present here a novel 3D automatic segmentation method for retinal OCT volume data. Briefly, to segment a boundary surface, two OCT volume datasets are obtained by using a 3D smoothing filter and a 3D differential filter. Their linear combination is then calculated to generate new volume data with an enhanced boundary surface, where pixel intensity, boundary position information, and intensity changes on both sides of the boundary surface are used simultaneously. Next, preliminary discrete boundary points are detected from the A-Scans of the volume data. Finally, surface smoothness constraints and a dynamic threshold are applied to obtain a smoothed boundary surface by correcting a small number of error points. Our method can extract retinal layer boundary surfaces sequentially with a decreasing search region of volume data. We performed automatic segmentation on eight human OCT volume datasets acquired from a commercial Spectralis OCT system, where each volume of data consisted of 97 OCT images with a resolution of 496 512; experimental results show that this method can accurately segment seven layer boundary surfaces in normal as well as some abnormal eyes.Comment: 27 pages, 19 figure

Repeatability of swept-source optical coherence tomography retinal and choroidal thickness measurements in neovascular age-related macular degeneration

BACKGROUND: The aim was to determine the intrasession repeatability of swept-source optical coherence tomography (SS-OCT)-derived retinal and choroidal thickness measurements in eyes with neovascular age-related macular degeneration (nAMD). METHODS: A prospective study consisting of patients with active nAMD enrolled in the Distance of Choroid Study at Moorfields Eye Hospital, London. Patients underwent three 12×9 mm macular raster scans using the deep range imaging (DRI) OCT-1 SS-OCT (Topcon) device in a single imaging session. Retinal and choroidal thicknesses were calculated for the ETDRS macular subfields. Repeatability was calculated according to methods described by Bland and Altman. RESULTS: 39 eyes of 39 patients with nAMD were included with a mean (±SD) age of 73.9 (±7.2) years. The mean (±SD) retinal thickness of the central macular subfield was 225.7 μm (±12.4 μm). The repeatability this subfield, expressed as a percentage of the mean central macular subfield thickness, was 23.2%. The percentage repeatability of the other macular subfields ranged from 13.2% to 28.7%. The intrasession coefficient of repeatability of choroidal thickness of the central macular subfield was 57.2 μm with a mean choroidal thickness (±SD) of 181 μm (±15.8 μm). CONCLUSIONS: This study suggests that a change >23.2% of retinal thickness and 57.2 μm choroidal thickness in the central macular subfield is required to distinguish true clinical change from measurement variability when using the DRI OCT-1 device to manage patients with nAMD

Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization

With the introduction of spectral-domain optical coherence tomography (OCT), resulting in a significant increase in acquisition speed, the fast and accurate segmentation of 3-D OCT scans has become evermore important. This paper presents a novel probabilistic approach, that models the appearance of retinal layers as well as the global shape variations of layer boundaries. Given an OCT scan, the full posterior distribution over segmentations is approximately inferred using a variational method enabling efficient probabilistic inference in terms of computationally tractable model components: Segmenting a full 3-D volume takes around a minute. Accurate segmentations demonstrate the benefit of using global shape regularization: We segmented 35 fovea-centered 3-D volumes with an average unsigned error of 2.46 $\pm$ 0.22 {\mu}m as well as 80 normal and 66 glaucomatous 2-D circular scans with errors of 2.92 $\pm$ 0.53 {\mu}m and 4.09 $\pm$ 0.98 {\mu}m respectively. Furthermore, we utilized the inferred posterior distribution to rate the quality of the segmentation, point out potentially erroneous regions and discriminate normal from pathological scans. No pre- or postprocessing was required and we used the same set of parameters for all data sets, underlining the robustness and out-of-the-box nature of our approach.Comment: Accepted for publication in Medical Image Analysis (MIA), Elsevie