Cynomolgus monkey's choroid reference database derived from hybrid deep learning optical coherence tomography segmentation.

Cynomolgus monkeys exhibit human-like features, such as a fovea, so they are often used in non-clinical research. Nevertheless, little is known about the natural variation of the choroidal thickness in relation to origin and sex. A combination of deep learning and a deterministic computer vision algorithm was applied for automatic segmentation of foveolar optical coherence tomography images in cynomolgus monkeys. The main evaluation parameters were choroidal thickness and surface area directed from the deepest point on OCT images within the fovea, marked as the nulla with regard to sex and origin. Reference choroid landmarks were set underneath the nulla and at 500 µm intervals laterally up to a distance of 2000 µm nasally and temporally, complemented by a sub-analysis of the central bouquet of cones. 203 animals contributed 374 eyes for a reference choroid database. The overall average central choroidal thickness was 193 µm with a coefficient of variation of 7.8%, and the overall mean surface area of the central bouquet temporally was 19,335 µm2 and nasally was 19,283 µm2. The choroidal thickness of the fovea appears relatively homogeneous between the sexes and the studied origins. However, considerable natural variation has been observed, which needs to be appreciated

Volume-rendered optical coherence tomography angiography during ocular interventions: Advocating for noninvasive intraoperative retinal perfusion monitoring.

We aimed to test for feasibility of volume-rendered optical coherence tomography angiography (OCTA) as a novel method for assessing/quantifying retinal vasculature during ocular procedures and to explore the potential for intraoperative use. Thirty patients undergoing periocular anaesthesia were enrolled, since published evidence suggests a reduction in ocular blood flow. Retinal perfusion was monitored based on planar OCTA image-derived data provided by a standard quantification algorithm and postprocessed/volume-rendered OCTA data using a custom software script. Overall, imaging procedures were successful, yet imaging artifacts occurred frequently. In interventional eyes, perfusion parameters decreased during anaesthesia. Planar image-derived and volume rendering-derived parameters were correlated. No correlation was found between perfusion parameters and a motion artifact score developed for this study, yet all perfusion parameters correlated with signal strength as displayed by the device. Concluding, volume-rendered OCTA allows for noninvasive three-dimensional retinal vasculature assessment/quantification in challenging surgical settings and appears generally feasible for intraoperative use

Uncovering of intraspecies macular heterogeneity in cynomolgus monkeys using hybrid machine learning optical coherence tomography image segmentation

The fovea is a depression in the center of the macula and is the site of the highest visual acuity. Optical coherence tomography (OCT) has contributed considerably in elucidating the pathologic changes in the fovea and is now being considered as an accompanying imaging method in drug development, such as antivascular endothelial growth factor and its safety profiling. Because animal numbers are limited in preclinical studies and automatized image evaluation tools have not yet been routinely employed, essential reference data describing the morphologic variations in macular thickness in laboratory cynomolgus monkeys are sparse to nonexistent. A hybrid machine learning algorithm was applied for automated OCT image processing and measurements of central retina thickness and surface area values. Morphological variations and the effects of sex and geographical origin were determined. Based on our findings, the fovea parameters are specific to the geographic origin. Despite morphological similarities among cynomolgus monkeys, considerable variations in the foveolar contour, even within the same species but from different geographic origins, were found. The results of the reference database show that not only the entire retinal thickness, but also the macular subfields, should be considered when designing preclinical studies and in the interpretation of foveal data

Reference database of total retinal vessel surface area derived from volume-rendered optical coherence tomography angiography

Optical coherence tomography angiography (OCTA) enables three-dimensional, high-resolution, depth-resolved flow to be distinguished from non-vessel tissue signals in the retina. Thus, it enables the quantification of the 3D surface area of the retinal vessel signal. Despite the widespread use of OCTA, no representative spatially rendered reference vessel surface area data are published. In this study, the OCTA vessel surface areas in 203 eyes of 107 healthy participants were measured in the 3D domain. A Generalized Linear Model (GLM) model analysis was performed to investigate the effects of sex, age, spherical equivalent, axial length, and visual acuity on the OCTA vessel surface area. The mean overall vessel surface area was 54.53 mm2 (range from 27.03 to 88.7 mm2). OCTA vessel surface area was slightly negatively correlated with age. However, the GLM model analysis identified axial length as having the strongest effect on OCTA vessel surface area. No significant correlations were found for sex or between left and right eyes. This is the first study to characterize three-dimensional vascular parameters in a population based on OCTA with respect to the vessel surface area

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

Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence

Machine learning has greatly facilitated the analysis of medical data, while the internal operations usually remain intransparent. To better comprehend these opaque procedures, a convolutional neural network for optical coherence tomography image segmentation was enhanced with a Traceable Relevance Explainability (T-REX) technique. The proposed application was based on three components: ground truth generation by multiple graders, calculation of Hamming distances among graders and the machine learning algorithm, as well as a smart data visualization ('neural recording'). An overall average variability of 1.75% between the human graders and the algorithm was found, slightly minor to 2.02% among human graders. The ambiguity in ground truth had noteworthy impact on machine learning results, which could be visualized. The convolutional neural network balanced between graders and allowed for modifiable predictions dependent on the compartment. Using the proposed T-REX setup, machine learning processes could be rendered more transparent and understandable, possibly leading to optimized applications

En face optical coherence tomography imaging ellipsoid zone regeneration in laser-induced and solar maculopathies

The purpose of the study was to analyze imaging findings in spectral domain en face optical coherence tomography (SD OCT) in patients with laser-induced and solar maculopathies focusing on the possible regeneration of the ellipsoid zone. In a retrospective case series of 3 patients (4 eyes) with solar maculopathy and 2 patients (3 eyes) with laser-induced maculopathy who underwent a comprehensive ocular examination, ellipsoid zone (EZ) was segmented from SD OCT data. Evaluation of EZ in en face OCT revealed a hyporeflective lesion surrounded by a hyperreflective border. The area of EZ alteration was measured manually in en face OCT. All patients showed partial EZ regeneration. Mean EZ alteration decreased from 0.12 mm$^{2}$ (range: 0.05-0.32) at baseline to 0.07 mm$^{2}$ (range: 0.01-0.22) at last follow-up (p = 0.018, mean follow-up: 372 days; range: 115-592). Mean best visual acuity (BVA) improved from 20/36 at baseline to 20/30 (p = 0.018). In conclusion, en face OCT imaging clearly delineated the area of EZ alteration in patients with laser-induced and solar maculopathies. Follow-up showed significant reformation of the EZ as well as improvement of BVA

New Frontiers in Noninvasive Analysis of Retinal Wall-to-Lumen Ratio by Retinal Vessel Wall Analysis

To compare measurement of wall-to-lumen ratio (WLR) by means of high-resolution adaptive optics imaging (AO) with intuitive to use retinal vessel wall (VW) analysis (VWA). Moreover, to validate the techniques by comparing WLR of healthy young (HY) with healthy older patients.; Ten retinal VW images of 13 HY (24 ± 2 years) and 16 healthy older (60 ± 8 years) were obtained with AO and VWA. The average of five measurements of VW, retinal vessel lumen and WLR of a single vessel from AO and VWA were calculated and compared.; WLR of AO and VWA images showed high correlations, r = 0.75, t(27) = 5.98,; P; < .001, but differed systematically (WLR: VWA, 40 ± 7% and AO, 35 ± 9%;; P; < .001). Comparable patterns were found for VW and vessel lumen. HY showed significantly lower WLR (AO, 31 ± 8% and VWA, 36 ± 8%) compared with healthy older (AO, 39 ± 9% [; P; = .012]; VWA, 42 ± 5% [; P; = .013]).; Assessment of WLR by VWA showed a good correlation with laborious analysis of the microstructure by high-resolution AO. Measurement of WLR in different age groups indicated good validity. Deviations in VW, vessel lumen, and WLR between AO and VWA can be explained by systematic differences in image scale and resolution. Future studies are needed to investigate the clinical relevance of microvascular WLR assessment by retinal VWA and its prognostic value.; Additional assessment of retinal WLR by use of digital VWA to evaluate microstructural remodeling may prove to be a valuable extension to the current use of retinal vessel diameters as biomarkers of cardiovascular risk

Interdevice variability of central corneal thickness measurement

PURPOSE To evaluate variability of central corneal thickness measurement (CCT) devices using a hitherto unprecedented number of CCT devices. METHODS CCT was measured consecutively in 122 normal corneas of 61 subjects with seven different devices using three distinct measurement technologies: Scheimpflug, Ultrasound, and Optical Coherence Tomography (OCT). Per device deviation from the mean CCT value per eye was used to determine which of the devices performed best, compared to the mean value. RESULTS Cirrus OCT yielded the lowest deviation. Deviations of the individual devices from the mean CCT of each eye were (OS/OD) 12.8±5.0/14.9±9.4 μm for Topcon noncontact specular microscopy (NCSM), 11.3±5.9/10.6±7.3 μm for Pentacam, 10.7±5.2/10.4±4.8 μm for Spectralis OCT, 6.0±3.9/6.2±4.9 μm for Topcon DRI OCT, 5.1±3.4/5.9±10.3 μm for AngioVue OCT, 4.8±4.1/5.7±4.6 μm for US pachymetry, and 4.2±3.2/5.7±4.6 μm for Cirrus OCT. The maximum differences between US pachymetry and the other devices were very high (up to 120 μm). CONCLUSION Central corneal thickness may be under- or overestimated due to high interdevice variations. Measuring CCT with one device only may lead to inappropriate clinical and surgical recommendations. OCT showed superior results