On Optical Coherence Tomography Images Research Articles

An Automated CAD System for Accurate Grading of Uveitis Using Optical Coherence Tomography Images

Uveitis is one of the leading causes of severe vision loss that can lead to blindness worldwide. Clinical records show that early and accurate detection of vitreous inflammation can potentially reduce the blindness rate. In this paper, a novel framework is proposed for automatic quantification of the vitreous on optical coherence tomography (OCT) with particular application for use in the grading of vitreous inflammation. The proposed pipeline consists of two stages, vitreous region segmentation followed by a neural network classifier. In the first stage, the vitreous region is automatically segmented using a U-net convolutional neural network (U-CNN). For the input of U-CNN, we utilized three novel image descriptors to account for the visual appearance similarity of the vitreous region and other tissues. Namely, we developed an adaptive appearance-based approach that utilizes a prior shape information, which consisted of a labeled dataset of the manually segmented images. This image descriptor is adaptively updated during segmentation and is integrated with the original greyscale image and a distance map image descriptor to construct an input fused image for the U-net segmentation stage. In the second stage, a fully connected neural network (FCNN) is proposed as a classifier to assess the vitreous inflammation severity. To achieve this task, a novel discriminatory feature of the segmented vitreous region is extracted. Namely, the signal intensities of the vitreous are represented by a cumulative distribution function (CDF). The constructed CDFs are then used to train and test the FCNN classifier for grading (grade from 0 to 3). The performance of the proposed pipeline is evaluated on a dataset of 200 OCT images. Our segmentation approach documented a higher performance than related methods, as evidenced by the Dice coefficient of 0.988 ± 0.01 and Hausdorff distance of 0.0003 mm ± 0.001 mm. On the other hand, the FCNN classification is evidenced by its average accuracy of 86%, which supports the benefits of the proposed pipeline as an aid for early and objective diagnosis of uvea inflammation.

Attention-based deep learning system for automated diagnoses of age-related macular degeneration in optical coherence tomography images.

The progression of age-related macular degeneration (AMD) is critical to treatment decisions in clinical practice. The disease can be classified into four categories namely, drusen, inactive choroidal neovascularization (CNV), active CNV, and normal, according to severity based on optical coherence tomography (OCT) images. Interpreting numerous OCT images is still time-consuming and labor-intensive, especially for the detection of the CNV activity. To address this problem, we developed a deep learning (DL) system based on OCT images, with the assistance of an attention mechanism, to automatically diagnose AMD. A public dataset (total 51,140 OCT images) and a private dataset (total 4951 OCT images) were utilized as a training dataset and a clinical validation dataset, respectively, to develop the DL model. A ResNet-34 DL model, with convolutional block attention module (CBAM) block integrated into each unit, was pretrained on the public dataset first and then finetuned on our private dataset to automatically diagnose AMD and assist clinical decision-making. GradCam, a visualization technique, was used to improve the interpretability of our model. The precision and recall of our model were, respectively, 84.3% and 87.3% for drusen, 81.2% and 80.0% for inactive CNV, 97.7% and 90.2% for active CNV, and 93.7% and 96.5% for normal. The area under the curve (AUC) corresponding to drusen, inactive CNV, active CNV, and normal for our model reached 0.9395, 0.9476, 0.9880, and 0.9925, respectively. The heatmaps indicated a high level of correspondence in the region of interest between our model and ophthalmologists on the diagnosis. The implementation of finetuning and attention mechanism improve the performance of our model in a distinct dataset. Our model successfully assisted in the diagnosis of AMD and achieved a detection precision and recall equal to those of ophthalmologists. The results of our study could contribute to the precise diagnosis of and decision-making regarding AMD.

Smooth borders between inner nuclear layer and outer plexiform layer predict fewer macular edema recurrences in branch retinal vein occlusion

We hypothesized the smoothness of the border between the inner nuclear layer (INL) and outer plexiform layer (OPL) associates with the frequency of macular edema (ME) recurrences secondary to branch retinal vein occlusion (BRVO). Thirty-seven consecutive eyes with BRVO treated with anti-vascular endothelial growth factor (VEGF) injections at 1-year follow-up were included. We manually traced the border between the INL and OPL within the 1.5-mm vertical line from the fovea on optical coherence tomography (OCT) images at the initial visit. The jagged ratio (JR), the border length divided by the spline curve length, was calculated. We performed univariate and multivariate regression analyses, including JR, patient characteristics, number of cystoid spaces in the INL, INL area, and outer retina area. Multivariate regression analysis showed JR significantly correlates with the total number of anti-VEGF injections (P < 0.0001). Moreover, the mean JR was significantly lower in the nine eyes receiving two or fewer injections than in the 28 eyes receiving three or more injections (1.02 ± 0.01 vs. 1.13 ± 0.06, P < 0.0001). A smooth border between the INL and the OPL on OCT images at the initial visit may be a biomarker for fewer ME recurrences in eyes with BRVO.

Automated delineation of corneal layers on OCT images using a boundary-guided CNN

Accurate segmentation of corneal layers depicted on optical coherence tomography (OCT) images is very helpful for quantitatively assessing and diagnosing corneal diseases (e.g., keratoconus and dry eye). In this study, we presented a novel boundary-guided convolutional neural network (CNN) architecture (BG-CNN) to simultaneously extract different corneal layers and delineate their boundaries. The developed BG-CNN architecture used three convolutional blocks to construct two network modules on the basis of the classical U-Net network. We trained and validated the network on a dataset consisting of 1,712 OCT images acquired on 121 subjects using a 10-fold cross-validation method. Our experiments showed an average dice similarity coefficient (DSC) of 0.9691, an intersection over union (IOU) of 0.9411, and a Hausdorff distance (HD) of 7.4423 pixels. Compared with several other classical networks, namely U-Net, Attention U-Net, Asymmetric U-Net, BiO-Net, CE-Net, CPFnte, M-Net, and Deeplabv3, on the same dataset, the developed network demonstrated a promising performance, suggesting its unique strength in segmenting corneal layers depicted on OCT images.

Three dimensional reconstruction of coronary artery stents from optical coherence tomography: experimental validation and clinical feasibility

The structural morphology of coronary stents (e.g. stent expansion, lumen scaffolding, strut apposition, tissue protrusion, side branch jailing, strut fracture), and the local hemodynamic environment after stent deployment are key determinants of procedural success and subsequent clinical outcomes. High-resolution intracoronary imaging has the potential to enable the geometrically accurate three-dimensional (3D) reconstruction of coronary stents. The aim of this work was to present a novel algorithm for 3D stent reconstruction of coronary artery stents based on optical coherence tomography (OCT) and angiography, and test experimentally its accuracy, reproducibility, clinical feasibility, and ability to perform computational fluid dynamics (CFD) studies. Our method has the following steps: 3D lumen reconstruction based on OCT and angiography, stent strut segmentation in OCT images, packaging, rotation and straightening of the segmented struts, planar unrolling of the segmented struts, planar stent wireframe reconstruction, rolling back of the planar stent wireframe to the 3D reconstructed lumen, and final stent volume reconstruction. We tested the accuracy and reproducibility of our method in stented patient-specific silicone models using micro-computed tomography (μCT) and stereoscopy as references. The clinical feasibility and CFD studies were performed in clinically stented coronary bifurcations. The experimental and clinical studies showed that our algorithm (1) can reproduce the complex spatial stent configuration with high precision and reproducibility, (2) is feasible in 3D reconstructing stents deployed in bifurcations, and (3) enables CFD studies to assess the local hemodynamic environment within the stent. Notably, the high accuracy of our algorithm was consistent across different stent designs and diameters. Our method coupled with patient-specific CFD studies can lay the ground for optimization of stenting procedures, patient-specific computational stenting simulations, and research and development of new stent scaffolds and stenting techniques.

A Novel Network With Parallel Resolution Encoders for the Diagnosis of Corneal Diseases.

To propose a deep-learning network for the diagnosis of two corneal diseases: Fuchs' endothlelial dystrophy and keratoconus, based on optical coherence tomography (OCT) images of the cornea. In this paper, we propose a novel network with parallel resolution-specific encoders and composite classification features to directly diagnose Fuchs' endothelial dystrophy and keratoconus using OCT images. Our proposed network consists of a multi-resolution input, multiple parallel encoders, and a composite of convolutional and dense features for classification. The purpose of using parallel resolution-specific encoders is to perform multi-resolution feature fusion. Also, using composite classification features enhances the dense feature learning. We implemented other related networks for comparison with our network and performed k-fold cross-validation on a dataset of 16,721 OCT images. We used saliency maps and sensitivity analysis to visualize our proposed network. The proposed network outperformed other networks with an image classification accuracy of 0.91 and a scan classification accuracy of 0.94. The visualizations show that our network learned better features than other networks. The proposed methods can potentially be a step towards the early diagnosis of corneal diseases, which is necessary to prevent their progression, hence, prevent loss of vision.

Detection of oedema on optical coherence tomography images using deep learning model trained on noisy clinical data.

To meet the demands imposed by the continuing growth of the Age-related macular degeneration (AMD) patient population, automation of follow-ups by detecting retinal oedema using deep learning might be a viable approach. However, preparing and labelling data for training is time consuming. In this study, we investigate the feasibility of training a convolutional neural network (CNN) to accurately detect retinal oedema on optical coherence tomography (OCT) images of AMD patients with labels derived directly from clinical treatment decisions, without extensive preprocessing or relabelling. A total of 50 439 OCT images with associated treatment information were retrieved from databases at the Department of Ophthalmology, Rigshospitalet, Copenhagen, Denmark between 01.06.2007 and 01.06.2018. A CNN was trained on the retrieved data with the recorded treatment decisions as labels and validated on a subset of the data relabelled by three ophthalmologists to denote presence of oedema. Moderate inter-grader agreement on presence of oedema in the relabelled data was found (76.4%). Despite different training and validation labels, the CNN performed on par with inter-grader agreement in detecting oedema on OCT images (AUC 0.97, accuracy 90.9%) and previously published models based on relabelled datasets. The level of performance shown by the current model might make it valuable in detecting disease activity in automated AMD patient follow-up systems. Our approach demonstrates that high accuracy is not necessarily constrained by incongruent training and validation labels. These results might encourage the use of existing clinical databases for development of deep learning based algorithms without labour-intensive preprocessing in the future.

DETECTION OF MORPHOLOGIC PATTERNS OF DIABETIC MACULAR EDEMA USING A DEEP LEARNING APPROACH BASED ON OPTICAL COHERENCE TOMOGRAPHY IMAGES.

To develop a deep learning (DL) model to detect morphologic patterns of diabetic macular edema (DME) based on optical coherence tomography (OCT) images. In the training set, 12,365 OCT images were extracted from a public data set and an ophthalmic center. A total of 656 OCT images were extracted from another ophthalmic center for external validation. The presence or absence of three OCT patterns of DME, including diffused retinal thickening, cystoid macular edema, and serous retinal detachment, was labeled with 1 or 0, respectively. A DL model was trained to detect three OCT patterns of DME. The occlusion test was applied for the visualization of the DL model. Applying 5-fold cross-validation method in internal validation, the area under the receiver operating characteristic curve for the detection of three OCT patterns (i.e., diffused retinal thickening, cystoid macular edema, and serous retinal detachment) was 0.971, 0.974, and 0.994, respectively, with an accuracy of 93.0%, 95.1%, and 98.8%, respectively, a sensitivity of 93.5%, 94.5%, and 96.7%, respectively, and a specificity of 92.3%, 95.6%, and 99.3%, respectively. In external validation, the area under the receiver operating characteristic curve was 0.970, 0.997, and 0.997, respectively, with an accuracy of 90.2%, 95.4%, and 95.9%, respectively, a sensitivity of 80.1%, 93.4%, and 94.9%, respectively, and a specificity of 97.6%, 97.2%, and 96.5%, respectively. The occlusion test showed that the DL model could successfully identify the pathologic regions most critical for detection. Our DL model demonstrated high accuracy and transparency in the detection of OCT patterns of DME. These results emphasized the potential of artificial intelligence in assisting clinical decision-making processes in patients with DME.

Aflibercept therapy for exudative age-related macular degeneration resistant to bevacizumab and ranibizumab

BackgroundDespite the good outcomes achieved with intravitreal angiogenic therapy, a subset of neovascular age-related macular degeneration (AMD) patients experience resistance to therapy after repeated injections. Switching drugs could offer benefit to this group of patients.PurposeTo determine visual and anatomical outcomes in a cohort of neovascular AMD patients resistant to repeated injections of bevacizumab/ranibizumab after switching to aflibercept therapy.MethodsThis was a retrospective chart review of patients who had a diagnosis of neovascular AMD and persistent intraretinal (IRF) and/or subretinal fluid (SRF) on optical coherence tomography (OCT) for at least 3 months despite monthly bevacizumab and/or ranibizumab injections prior to transition to aflibercept. We reviewed patients’ records and OCT images obtained at baseline, 1, 3, 6 and 12 months after transition to aflibercept. Data collected included demographics, best-corrected visual acuity (BCVA), number of injections received and the occurrence of any adverse events. Studied OCT parameters included central macular thickness (CMT) values and the presence or absence of SRF, IRF and/or pigment epithelial detachment (PED) at each visit.ResultsWe included 53 eyes of 48 patients. Mean change in BCVA from baseline was 0.05 ± 0.13 (P = 0.01) at M1, 0.04 ± 0.16 (P = 0.08) at M3, 0.01 ± 0.22 (P = 0.9) at M6, and 0.02 ± 0.28 (P = 1) at M12, while the mean change in CMT from baseline was 64 ± 75 μm (P < 0.0001) at M1, 42 ± 85 μm (P = 0.002) at M3, 47 ± 69 μm (P < 0.0001) at M6, and 46 ± 99 μm (P = 0.001) at M12. The percentage of eyes with SRF decreased from 77.4% at baseline to 39.6% at M1, then increased to 47.2% at M3, then decreased to 43.4% at M6, and to 41.5% at M12 (All p < 0.001, compared to baseline). Compared to baseline, there was a statistically significant decrease in the percentage of eyes having IRF from 47.2 to 20.8% at M1 (p < 0.001), 30.2% at M3, 24.5% at M6 and 26.4% at M12 (p < 0.01, each). The number of bevacizumab and/or ranibizumab injections (7.36 ± 1.85) was significantly higher than that of aflibercept (6.47 ± 2.45, p = 0.001). A significant direct relationship between CMT reduction and BCVA improvement was demonstrated at M1 (p = 0.01, r = 0.36), M3 (p = 0.03, r = 0.30) and M12 (p = 0.03, r = 0.30). Eyes with IRF had significantly poorer BCVA than eyes without IRF at baseline (p = 0.02) and M3 (p = 0.04).ConclusionSwitching to intravitreal aflibercept therapy in a cohort of neovascular AMD patients resistant to chronic bevacizumab and/or ranibizumab injections can lead to significant visual improvement in the short term and sustained reduction of central macular thickness over 1 year of followup.

Quantification of residual ellipsoid zone and its correlation with visual functions in patients with cone-rod dystrophy

Cone-rod dystrophy (CORD) is one of the inherited retinal diseases that result in central visual field deterioration and decreased visual acuity (VA). In CORD patients, impaired photoreceptor cells are observed as the disruption of ellipsoid zone (EZ) on optical coherence tomography (OCT) images. In the present study, we calculated the index of residual EZ (rEZ) to quantify the function of photoreceptor cells and investigated the correlation between rEZ index and visual functions. Twenty-six eyes of 13 patients with clinical suspicion of CORD were examined. Visual field was tested with the Humphrey Visual Field Analyzer (HFA 10-2 program). We simultaneously obtained OCT images and calculated the area of decreased EZ intensity (EZa). Using the binarized OCT images, the percentage of the rEZ in a 3 × 3 mm area surrounding the macula was analyzed. To clarify interrator reproducibility, intraclass correlation coefficient (ICC) was calculated. Moreover, we investigated the association between OCT parameters and VA as well as the mean deviation (MD) value measured with HFA. The mean age of the patients was 48.5 ± 16.9 years. The mean central retinal thickness was 122.7 ± 73.2 μm. The mean EZa and rEZ were 22.2 ± 23.6 μm2 and 0.35 ± 0.31, respectively. The ICC of each rEZ index was 0.91 (95% CI: 0.89 < ICC < 0.93). Multivariate analysis indicated rEZ was significantly related to logMAR VA (p = 0.05) and rEZ and EZa were associated with the MD value (p = 0.014 and p = 0.009, linear mixed model). Furthermore, rEZ was also associated with photopic a- and b-wave amplitudes (p = 0.027 and p = 0.0024, respectively, linear mixed model). Taken together, the current results suggested the usefulness of rEZ quantification for predicting visual functions in CORD patients.

Silicone microbubbles after anti-vascular endothelial growth factor injections in patients with wet age-related macular degeneration: incidence, quantification and secondary optical coherence tomography artfacts.

To report the incidence and quantity of silicone oil microbubbles and the relationship with the number of intravitreal anti-vascular endothelial growth factor (VEGF) injections and evaluate if microbubbles induce artefacts on optical coherence tomography (OCT) images. Observational, descriptive, cross-sectional study. Patients with wet age-related macular degeneration were included who had been treated for 1year minimally with anti-VEGF injections repackaged in the hospital pharmacy. Detection and quantification of silicone microbubbles by mydriatic biomicroscopic examination were conducted 1month after the last injection. The numbers of microbubbles were quantified on a scale of 0-3: 0, none; 1 scarce (1-10 microbubbles); 2 moderate (10-30); or 3 numerous (>30). Shadowing on OCT images was classified as 0-3: 0, none; 1 obscuring some retinal layers; 2 obscuring all retinal layers; or 3 obscuring the retinal thickness. The study included 142 eyes of 98 patients (mean age, 82.4years+7.3; range, 65-97) treated with 2377 injections. Microbubbles were detected in 127 (89.4%) eyes, 62 (43.6%) with numerous microbubbles and 36 (25.4%) and 29 (20.4%), respectively, with scarce and moderate numbers. A positive correlation was found between the numbers of injections and intravitreal silicone (rho, 0.7). Optical coherence tomography (OCT) artefacts were detected in 11 eyes; the artefacts obscured all retinal layers in three eyes. No significant relationship could be established between the appearance of floaters and the microbubbles. The presence and number of silicone microbubbles were correlated with the number of intravitreal injections. Microbubbles can produce OCT artefacts, which can hinder the treatment decision.

A multi-center study of prediction of macular hole status after vitrectomy and internal limiting membrane peeling by a deep learning model

BackgroundTo develop a deep learning (DL) model for prediction of idiopathic macular hole (MH) status after vitrectomy and internal limiting membrane peeling (VILMP) based on optical coherence tomography (OCT) images from four ophthalmic centers.MethodsEyes followed up at 1 month after VILMP for full-thickness MH were included. In the internal training set, 920 preoperative macular OCT images (as the input) and post-operative status of MH (closed or open, as the output) of 256 eyes from two ophthalmic centers were used to train the DL model using VGG16 algorithm. In the external validation set, 72 preoperative macular OCT images of 36 MH eyes treated by VILMP from another two ophthalmic centers were used to validate the prediction accuracy of the DL model.ResultsIn internal training, the mean of overall accuracy for prediction of MH status after VILMP was 84.6% with a mean area under the receiver operating characteristic (ROC) curve (AUC) of 91.04% (sensitivity 85.37% and specificity 81.99%). In external validation, the overall accuracy of predicting MH status after VILMP was 84.7% with an AUC of 89.32% (sensitivity 83.33% and specificity 87.50%). The heatmaps showed that the area critical for prediction was at the central macula, mainly at the MH and its adjacent retina.ConclusionsThe DL model trained by preoperative macular OCT images can be used to predict postoperative MH status after VILMP. The prediction accuracy of our DL model has been validated by multiple ophthalmic centers.

Denoising Performance Evaluation of Automated Age-Related Macular Degeneration Detection on Optical Coherence Tomography Images

Automated detection of eye diseases using artificial intelligence techniques on optical coherence tomography (OCT) images is widely researched in the field of ophthalmology. Such detections are usually performed with the aid of computers. Using high-level simulations, this study investigates and evaluates three automated age-related macular degeneration (AMD) detection flows in terms of computation time and detection accuracy for future hardware-accelerated designs of intelligent and portable OCT systems. In this study, a block-matching and 3-Dimension filter (BM3DF), a hybrid median filter (HMF), and an adaptive wiener filter (AWF) are used to denoise the OCT images. Support vector machine (SVM), AlexNet, GoogLeNet, and Inception-ResNet are employed for AMD detection. Moreover, Local binary patterns, linear configuration patterns, and transfer learning techniques are used to extract image features. Simulation results reveal that machine-learning-based automated AMD detection realizes a high detection accuracy of 95.91% accompanied by low computation time when using the HMF rather than the BM3DF. When considering deep-learning-based automated AMD detection, the combination of HMF and Inception-ResNet achieves the highest detection accuracy of 98.64% but is accompanied by a dramatic increase in computation time. However, only AlexNet achieves a detection accuracy of 96.40%, accompanied by low computation time. In this study, the tradeoffs between the computation time and detection accuracy have been revealed by comparing the denoising methods for the distinct automated AMD detections.

Anti-VEGF-therapy of fibrovascular and serous-vascularized pigment epithelial detachment in neovascular AMD : A retrospective five-year-analysis

HintergrundDie neovaskuläre altersabhängige Makuladegeneration (nAMD) ist die häufigste Ursache für das Auftreten einer Pigmentepithelabhebung (PED). Die Therapie der fibrovaskulären PED (fPED) und serös-vaskularisierten PED (svPED) mit intravitrealen VEGF(„vascular endothelial growth factor“)-Inhibitoren hat in der klinischen Praxis eine eingeschränkte Prognose.Ziel der ArbeitDie Datenlage zum PED-Langzeitverlauf ist überschaubar. Diese Arbeit erfasst den Verlauf therapierter PEDs bei nAMD über einen Zeitraum von 5 Jahren.MethodikBei allen Augen, die im Zeitraum von 2006 bis 2015 aufgrund einer fPED oder svPED eine Behandlung mit Anti-VEGF-Präparaten erhielten, erfolgte eine retrospektive Analyse des klinischen Verlaufs und der Morphologie mittels optischer Kohärenztomographie (OCT). Es galten die folgenden Einschlusskriterien: OCT-Nachweis einer PED, angiographische Bestätigung einer nAMD, klinische Dokumentation über 5 Jahre sowie gute Bildqualität.ErgebnisseBei 23 Augen von 22 Patienten wurden die Einschlusskriterien erfüllt. Nach 5 Jahren zeigte sich im Gesamtkollektiv und in der Subgruppe der Augen mit fPED eine signifikante Verschlechterung des Visus (p = 0,007; p = 0,045). Bei den Augen mit svPED war die Visusabnahme nicht signifikant (p = 0,097). Im Gesamtkollektiv wurde eine statistisch signifikante Reduktion der PED-Höhe (p = 0,006) bei gleichzeitig signifikanter Zunahme des PED-Durchmessers (p = 0,002) gemessen, wobei innerhalb der Subgruppen die Abnahme der PED-Höhe und Zunahme des PED-Durchmessers nur für die Messwerte der svPED signifikant waren (p = 0,004; p = 0,013). Bei den Augen mit fPED war die Veränderung der OCT-Parameter nicht signifikant (p = 0,616; p = 0,097). Bei 17 (74 %) der Augen fand sich bei der finalen OCT-Beurteilung eine Fibrose oder Atrophie.DiskussionNach 5 Jahren Anti-VEGF-Behandlung bei nAMD-assoziierter PED zeigten sich in der Hälfte der Fälle eine Visusabnahme und in drei Viertel der Augen eine ungünstige OCT-Morphologie. Die mittlere Anzahl an Injektionen und Visiten war niedriger als in klinischen Studien und anderen Real-life-Erhebungen. Insgesamt beobachteten wir eine Unterbehandlung mit schlechterem funktionellem und anatomischem Outcome in unserer klinischen Praxis verglichen mit anderen Studien.

Correlation between the Angiographic Choriocapillaris and the Structural Inner Choroid

ABSTRACT Purpose: To assess the correlation between the choriocapillaris (CC) on optical coherence tomography (OCT) angiography and the inner choroid (IC) on structural OCT. Methods: In this prospective, cross-sectional, observational, pilot study, consecutive healthy subjects were imaged with a 6x6mm macular scan using OCTA PlexElite. Angiographic images of the CC were isolated using 10 μm-thick slab starting 21 μm posterior to the automatically segmented retinal pigment epithelium (RPE) band centerline. The structural en face OCT images of the IC were isolated using five 10 μm-thick slabs starting 11 to 51 μm posterior to the automatically segmented RPE band centerline. Retinal vessels masking was obtained by two methods: automated OCTA superficial vessel slab and automated structural OCT outer retina to CC (ORCC) slab. The en face OCT images were binarized for computation of the stromal index (SI). The flow deficits (FD) of the angiographic CC were correlated with the SI of the structural en face OCTs of the IC. Results: Twenty eyes of 20 subjects were enrolled. The strongest correlation was observed between the CC FD and the inner choroid SI of the 31–41 slab (IC SI31-41) using the ORCC mask (R = 0.603, p = .005). Similar results were observed when the analysis was repeated when using IC SI31-41 values obtained with the superficial vessels mask (R = 0.561, p = .01). Conclusions: The angiographic CC FD was significantly correlated with the structural inner choroidal stromal index. These findings suggest that there may be an opportunity to use structural OCT data to analyze the CC, which may allow longitudinal assessment from historical structural OCT datasets.

Deep learning-based classification and segmentation of retinal cavitations on optical coherence tomography images of macular telangiectasia type 2

AimTo develop a fully automatic algorithm to segment retinal cavitations on optical coherence tomography (OCT) images of macular telangiectasia type 2 (MacTel2).MethodsThe dataset consisted of 99 eyes from 67 participants...

Real-time assessment and characterization of immobilized lipase onto a natural matrix and qualitative reaction kinetic studies using swept-source optical coherence tomography

The current study presents a method based on Optical Coherence Tomography (OCT) for non-destructive, real-time analysis and portrayal of immobilization efficacy for lipase on a natural matrix namely, eggshell. Subsequently, qualitative biochemical reaction kinetics of immobilized lipase was also studied. Successful immobilization of lipase on eggshell was confirmed by the presence of a clear peak in 'A' scan of OCT image. From immobilization kinetics it is clearly observed that the thickness of the highest peak of the A-scan increases significantly and peak intensity saturated after 90min of incubation. Hydrolysis of oil using immobilized lipase indicated that the release of free fatty acids increased up to 8h during reaction and the result was in accordance with the 'B' scan data of the OCT system. Changes in scattering coefficient-based analysis were performed with respect to incubation time to showcase the immobilization process and hydrolysis reaction of lipase. Scanning electron microscope analysis with smoother surface indicated presence of lipase on eggshell matrices, with no further change after oil hydrolysis.

Non-proliferative type II macular telangiectasia variant with subfoveal detachment: role of anti-VEGF therapy

AimsTo describe a rare variant of non-proliferative stage of type II macular telangiectasia (MacTel2) with the presence of subfoveal detachment (SFD) and its response to intravitreal antivascular endothelial growth factor...

Local Glaucomatous Defects of the Circumpapillary Retinal Nerve Fiber Layer Show a Variety of Patterns of Progression.

The region of glaucomatous progression, seen on optical coherence tomography (OCT) images of the circumpapillary retinal nerve fiber layer (cRNFL), increases in width and depth in all eyes, but shows a variety of different patterns of loss across eyes. The purpose of this study was to examine the patterns of cRNFL loss secondary to glaucomatous progression in a region associated with the superior hemifield of the 24-2/30-2 visual field (VF). Twenty-four eyes (20 patients) with a diagnosis of glaucoma and evidence of progression on OCT had OCT disc cube scans on at least 3 separate visits (mean follow-up 7.4 y; range: 3.9 to 11.4). Circumpapillary b-scans were derived after enface images were aligned to assure that the study region (ie, 0 to -135 degrees, where 0 degree is 9 o'clock, on a right eye) coincided. Within this region, a region of progression (ROP) was defined based on the loss in cRNFL thickness between the first and subsequent visits. The width of the ROP was determined, along with the locations of its leading (close to fixation) and trailing edges. In addition, for each ROP, the location and depth at the point of maximal loss, total loss, and average remaining retinal nerve fiber layer were measured. The ROP proceeded both toward and away from fixation. Across eyes, the ROP varied widely in width (32 to 131 degrees, mean 82.7 degrees), location, and loss at point of deepest loss (22 to 99 μm, mean 52.9 μm), as well as total cRNFL loss. All eyes showed a widening and deepening of the ROP, but a variety of different patterns of progressive cRNFL loss. Thus, one should expect considerable variation in patterns of VF loss. Furthermore, conventional metrics (global or quadrant cRNFL thickness) do not fully depict the progressive changes that can be appreciated by inspecting OCT images.

Impregnation of healthy nail tissue with optical clearing agents for improved optical coherence tomography imaging.

The impact of optical tissue clearing on optical coherence tomography (OCT) for nail tissue imaging has not been investigated. This study seeks to compare the effects of an emollient and water on visualization of micromorphology and morphometric outcomes. Thirty-six healthy nail plates were processed with a fractional CO2 laser, imaged with OCT, and measured with calipers in duplicates. All samples were reassessed after 12-hour long sequential immersion in water and an emollient (Crodamol™ STS). OCT images were evaluated for thickness and scattering signal of the nail. Emollient-impregnation caused stronger scatter responses (P<.0001) and decreased nail thickness (MD 45μm, P<.0001) measured on OCT. Caliper-derived measurements were not affected by Crodamol™ (MD 11μm, P=.5538). Hydration increased nail thickness on OCT (MD 49μm, P<.0001) but reduced thickness measurements taken with calipers (MD 41μm, P<.0001). Emollient-impregnation improved visualization of onychocytes compared with dry (P=.0209) and hydrated samples (P<.0001), and reduced occurrence of refractive artifacts (P<.0001). The use of an emollient for OCT imaging can enhance nail tissue visualization without significant effects on caliper measurements. Hydration of nails, in contrast with emollient-impregnation, may lead to disagreement between caliper- and OCT-measured nail thickness and should be practiced cautiously.