A Novel Predictive Model Utilizing Retinal Microstructural Features for Estimating Survival Outcome in Patients with Glioblastoma.

Glioblastoma is a highly aggressive brain tumor with poor prognosis despite surgery and chemoradiation. The visual sequelae of glioblastoma have not been well characterized. This study assessed visual outcomes in glioblastoma patients through neuro-ophthalmic exams, imaging of the retinal microstructures/microvasculature, and perimetry. A total of 19 patients (9 male, 10 female, average age at diagnosis 69 years) were enrolled. Best-corrected visual acuity ranged from 20/20-20/50. Occipital tumors showed worse visual fields than frontal tumors (mean deviation - 14.9 and - 0.23, respectively, p < 0.0001). Those with overall survival (OS) < 15 months demonstrated thinner retinal nerve fiber layer and ganglion cell complex (p < 0.0001) and enlarged foveal avascular zone starting from 4 months post-diagnosis (p = 0.006). There was no significant difference between eyes ipsilateral and contralateral to radiation fields (average doses were 1370 cGy and 1180 cGy, respectively, p = 0.42). A machine learning algorithm using retinal microstructure and visual fields predicted patients with long (≥ 15 months) progression free and overall survival with 78% accuracy. Glioblastoma patients frequently present with visual field defects despite normal visual acuity. Patients with poor survival duration demonstrated significant retinal thinning and decreased microvascular density. A machine learning algorithm predicted survival; further validation is warranted.

DIRECTIONAL OPTICAL COHERENCE TOMOGRAPHY IMAGING OF MACULAR PATHOLOGY.

PURPOSE: To survey the impact of directional reflectivity on structures within optical coherence tomography images in retinal pathology. METHODS: Sets of commercial optical coherence tomography images taken from multiple pupil positions were analyzed. These directional optical coherence tomography sets revealed directionally reflective structures within the retina. After ensuring sufficient image quality, resulting hybrid and composite images were characterized by assessing the Henle fiber layer, outer nuclear layer, ellipsoid zone, and interdigitation zone. Additionally, hybrid images were reviewed for novel directionally reflective pathological features. RESULTS: Cross-sectional directional optical coherence tomography image sets were obtained in 75 eyes of 58 patients having a broad range of retinal pathologies. All cases showed improved visualization of the outer nuclear layer/Henle fiber layer interface, and outer nuclear layer thinning was, therefore, more apparent in several cases. The ellipsoid zone and interdigitation zone also demonstrated attenuation where a geometric impact of underlying pathology affected their orientation. Misdirected photoreceptors were also noted as a consistent direction-dependent change in ellipsoid zone reflectivity between regions of normal and absent ellipsoid zone. CONCLUSION: Directional optical coherence tomography enhances the understanding of retinal anatomy and pathology. This optical contrast yields more accurate identification of retinal structures and possible imaging biomarkers for photoreceptor-related pathology.

Can Glaucoma Suspect Data Help to Improve the Performance of Glaucoma Diagnosis?

Purpose: The presence of imbalanced datasets in medical applications can negatively affect deep learning methods. This study aims to investigate how the performance of convolutional neural networks (CNNs) for glaucoma diagnosis can be improved by addressing imbalanced learning issues through utilizing glaucoma suspect samples, which are often excluded from studies because they are a mixture of healthy and preperimetric glaucomatous eyes, in a semi-supervised learning approach. Methods: A baseline 3D CNN was developed and trained on a real-world glaucoma dataset, which is naturally imbalanced (like many other real-world medical datasets). Then, three methods, including reweighting samples, data resampling to form balanced batches, and semi-supervised learning on glaucoma suspect data were applied to practically assess their impacts on the performances of the trained methods. Results: The proposed method achieved a mean accuracy of 95.24%, an F1 score of 97.42%, and an area under the curve of receiver operating characteristic (AUC ROC) of 95.64%, whereas the corresponding results for the traditional supervised training using weighted cross-entropy loss were 92.88%, 96.12%, and 92.72%, respectively. The obtained results show statistically significant improvements in all metrics. Conclusions: Exploiting glaucoma suspect eyes in a semi-supervised learning method coupled with resampling can improve glaucoma diagnosis performance by mitigating imbalanced learning issues. Translational Relevance: Clinical imbalanced datasets may negatively affect medical applications of deep learning. Utilizing data with uncertain diagnosis, such as glaucoma suspects, through a combination of semi-supervised learning and class-imbalanced learning strategies can partially address the problems of having limited data and learning on imbalanced datasets.

A Multi-Modal AI-Driven Cohort Selection Tool to Predict Suboptimal Non-Responders to Aflibercept Loading-Phase for Neovascular Age-Related Macular Degeneration: PRECISE Study Report 1.

Patients diagnosed with exudative neovascular age-related macular degeneration are commonly treated with anti-vascular endothelial growth factor (anti-VEGF) agents. However, response to treatment is heterogeneous, without a clinical explanation. Predicting suboptimal response at baseline will enable more efficient clinical trial designs for novel, future interventions and facilitate individualised therapies. In this multicentre study, we trained a multi-modal artificial intelligence (AI) system to identify suboptimal responders to the loading-phase of the anti-VEGF agent aflibercept from baseline characteristics. We collected clinical features and optical coherence tomography scans from 1720 eyes of 1612 patients between 2019 and 2021. We evaluated our AI system as a patient selection method by emulating hypothetical clinical trials of different sizes based on our test set. Our method detected up to 57.6% more suboptimal responders than random selection, and up to 24.2% more than any alternative selection criteria tested. Applying this method to the entry process of candidates into randomised controlled trials may contribute to the success of such trials and further inform personalised care.

Mixed multiscale BM4D for three-dimensional optical coherence tomography denoising.

A multiscale extension for the well-known block matching and 4D filtering (BM4D) method is proposed by analyzing and extending the wavelet subbands denoising method in such a way that the proposed method avoids directly denoising detail subbands, which considerably simplifies the computations and makes the multiscale processing feasible in 3D. To this end, we first derive the multiscale construction method in 2D and propose multiscale extensions for three 2D natural image denoising methods. Then, the derivation is extended to 3D by proposing mixed multiscale BM4D (mmBM4D) for optical coherence tomography (OCT) image denoising. We tested mmBM4D on three public OCT datasets captured by various imaging devices. The experiments revealed that mmBM4D significantly outperforms its original counterpart and performs on par with the state-of-the-art OCT denoising methods. In terms of peak-signal-to-noise-ratio (PSNR), mmBM4D surpasses the original BM4D by more than 0.68 decibels over the first dataset. In the second and third datasets, significant improvements in the mean to standard deviation ratio, contrast to noise ratio, and equivalent number of looks were achieved. Furthermore, on the downstream task of retinal layer segmentation, the layer quality preservation of the compared OCT denoising methods is evaluated.

Directional Reflectivity of the Ellipsoid Zone in Dry Age-Related Macular Degeneration.

BACKGROUND AND OBJECTIVE: Ellipsoid zone (EZ) reflectivity on optical coherence tomography (OCT) is affected by the orientation of the scanning beam. The authors sought to determine how directional reflectivity changes in dry age-related macular degeneration (AMD). PATIENTS AND METHODS: Retrospective image analysis included 17 control and 20 dry AMD subjects. Directional OCT (D-OCT) was performed using multiple displaced pupil entrance positions. EZ pixel values and apparent incidence angles were measured. RESULTS: EZ reflectivity decreased in off-axis scans in controls (P < .001), AMD areas between drusen (P < .001), and AMD areas overlying drusen (P < .001). The magnitude of decrement in EZ reflectivity was significantly higher when incidence angles exceeded 10° in controls than in AMD areas between drusen (P = .024). CONCLUSION: EZ reflectivity in dry AMD may vary by incident angle of light less than in controls, possibly indicating alteration of photoreceptor orientation or integrity. [Ophthalmic Surg Lasers Imaging Retina. 2021;52:145-152.].

Estimating Global Visual Field Indices in Glaucoma by Combining Macula and Optic Disc OCT Scans Using 3-Dimensional Convolutional Neural Networks.

PURPOSE: To evaluate the accuracy at which visual field global indices could be estimated from OCT scans of the retina using deep neural networks and to quantify the contributions to the estimates by the macula (MAC) and the optic nerve head (ONH). DESIGN: Observational cohort study. PARTICIPANTS: A total of 10 370 eyes from 109 healthy patients, 697 glaucoma suspects, and 872 patients with glaucoma over multiple visits (median = 3). METHODS: Three-dimensional convolutional neural networks were trained to estimate global visual field indices derived from automated Humphrey perimetry (SITA 24-2) tests (Zeiss, Dublin, CA), using OCT scans centered on MAC, ONH, or both (MAC + ONH) as inputs. MAIN OUTCOME MEASURES: Spearman's rank correlation coefficients, Pearson's correlation coefficient, and absolute errors calculated for 2 indices: visual field index (VFI) and mean deviation (MD). RESULTS: The MAC + ONH achieved 0.76 Spearman's correlation coefficient and 0.87 Pearson's correlation for VFI and MD. Median absolute error was 2.7 for VFI and 1.57 decibels (dB) for MD. Separate MAC or ONH estimates were significantly less correlated and less accurate. Accuracy was dependent on the OCT signal strength and the stage of glaucoma severity. CONCLUSIONS: The accuracy of global visual field indices estimate is improved by integrating information from MAC and ONH in advanced glaucoma, suggesting that structural changes of the 2 regions have different time courses in the disease severity spectrum.

Attention-Guided 3D-CNN Framework for Glaucoma Detection and Structural-Functional Association Using Volumetric Images.

The direct analysis of 3D Optical Coherence Tomography (OCT) volumes enables deep learning models (DL) to learn spatial structural information and discover new bio-markers that are relevant to glaucoma. Downsampling 3D input volumes is the state-of-art solution to accommodate for the limited number of training volumes as well as the available computing resources. However, this limits the network's ability to learn from small retinal structures in OCT volumes. In this paper, our goal is to improve the performance by providing guidance to DL model during training in order to learn from finer ocular structures in 3D OCT volumes. Therefore, we propose an end-to-end attention guided 3D DL model for glaucoma detection and estimating visual function from retinal structures. The model consists of three pathways with the same network architecture but different inputs. One input is the original 3D-OCT cube and the other two are computed during training guided by the 3D gradient class activation heatmaps. Each pathway outputs the class-label and the whole model is trained concurrently to minimize the sum of losses from three pathways. The final output is obtained by fusing the predictions of the three pathways. Also, to explore the robustness and generalizability of the proposed model, we apply the model on a classification task for glaucoma detection as well as a regression task to estimate visual field index (VFI) (a value between 0 and 100). A 5-fold cross-validation with a total of 3782 and 10,370 OCT scans is used to train and evaluate the classification and regression models, respectively. The glaucoma detection model achieved an area under the curve (AUC) of 93.8% compared with 86.8% for a baseline model without the attention-guided component. The model also outperformed six different feature based machine learning approaches that use scanner computed measurements for training. Further, we also assessed the contribution of different retinal layers that are relevant to glaucoma. The VFI estimation model achieved a Pearson correlation and median absolute error of 0.75 and 3.6%, respectively, for a test set of size 3100 cubes.

Forecasting Retinal Nerve Fiber Layer Thickness from Multimodal Temporal Data Incorporating OCT Volumes.

Purpose: The purpose of this study was to develop a machine learning model to forecast future circumpapillary retinal nerve fiber layer (cpRNFL) thickness in eyes of healthy, glaucoma suspect, and glaucoma participants from multimodal temporal data. Design: Retrospective analysis of a longitudinal clinical cohort. Participants: Longitudinal clinical cohort of healthy, glaucoma suspect, and glaucoma participants. Methods: The forecasting models used multimodal patient information including clinical (age and intraocular pressure), structural (cpRNFL thickness derived from scans as well as deep learning-derived OCT image features), and functional (visual field test parameters) data and the intervisit interval for prediction of cpRNFL thickness at the next visit. Four models were developed based on the number of visits used (n = 1 to 4). Longitudinal data from 1089 participants (mean observation period, 3.65±1.73 years) was used with 80% of the cohort for the development of the models. The results of our models were compared with those of a commonly adopted linear regression model, which we refer to here as linear trend-based estimation (LTBE). Main Outcome Measures: The mean absolute difference and Pearson's correlation coefficient between the true and forecasted values of the cpRNFL in the healthy, glaucoma suspect, and glaucoma patients. Results: The best forecasting model of cpRNFL was obtained using 3 visits and incorporated deep learning-derived OCT image features. The mean error was 1.10±0.60 µm, 1.79±1.73 µm, and 1.87±1.85 µm in eyes of healthy, glaucoma suspect, and glaucoma participants, respectively. Our method significantly outperformed the LTBE model for glaucoma suspect and glaucoma participants (P < 0.001), which showed a mean error of 1.55±1.16 µm, 2.4±2.67 µm, and 3.02±3.06 µm in the 3 groups, respectively. The Pearson's correlation coefficient between the forecasted value and the measured thickness was ρ = 0.96 (P < 0.01), ρ = 0.95 (P < 0.01), and ρ = 0.96 (P < 0.01) for the 3 groups, respectively. Conclusions: The performance of the proposed forecasting model for cpRNFL is consistent across glaucoma suspect and glaucoma patients, which implies the robustness of the developed model against the disease state. These forecasted values may be useful to personalize patient care by determining the most appropriate intervisit schedule for timely interventions.

Artificial Intelligence for Clinical Trial Design.

Clinical trials consume the latter half of the 10 to 15 year, 1.5-2.0 billion USD, development cycle for bringing a single new drug to market. Hence, a failed trial sinks not only the investment into the trial itself but also the preclinical development costs, rendering the loss per failed clinical trial at 800 million to 1.4 billion USD. Suboptimal patient cohort selection and recruiting techniques, paired with the inability to monitor patients effectively during trials, are two of the main causes for high trial failure rates: only one of 10 compounds entering a clinical trial reaches the market. We explain how recent advances in artificial intelligence (AI) can be used to reshape key steps of clinical trial design towards increasing trial success rates.

A feature agnostic approach for glaucoma detection in OCT volumes.

Optical coherence tomography (OCT) based measurements of retinal layer thickness, such as the retinal nerve fibre layer (RNFL) and the ganglion cell with inner plexiform layer (GCIPL) are commonly employed for the diagnosis and monitoring of glaucoma. Previously, machine learning techniques have relied on segmentation-based imaging features such as the peripapillary RNFL thickness and the cup-to-disc ratio. Here, we propose a deep learning technique that classifies eyes as healthy or glaucomatous directly from raw, unsegmented OCT volumes of the optic nerve head (ONH) using a 3D Convolutional Neural Network (CNN). We compared the accuracy of this technique with various feature-based machine learning algorithms and demonstrated the superiority of the proposed deep learning based method. Logistic regression was found to be the best performing classical machine learning technique with an AUC of 0.89. In direct comparison, the deep learning approach achieved a substantially higher AUC of 0.94 with the additional advantage of providing insight into which regions of an OCT volume are important for glaucoma detection. Computing Class Activation Maps (CAM), we found that the CNN identified neuroretinal rim and optic disc cupping as well as the lamina cribrosa (LC) and its surrounding areas as the regions significantly associated with the glaucoma classification. These regions anatomically correspond to the well established and commonly used clinical markers for glaucoma diagnosis such as increased cup volume, cup diameter, and neuroretinal rim thinning at the superior and inferior segments.

Automated summarisation of SDOCT volumes using deep learning: Transfer learning vs de novo trained networks.

Spectral-domain optical coherence tomography (SDOCT) is a non-invasive imaging modality that generates high-resolution volumetric images. This modality finds widespread usage in ophthalmology for the diagnosis and management of various ocular conditions. The volumes generated can contain 200 or more B-scans. Manual inspection of such large quantity of scans is time consuming and error prone in most clinical settings. Here, we present a method for the generation of visual summaries of SDOCT volumes, wherein a small set of B-scans that highlight the most clinically relevant features in a volume are extracted. The method was trained and evaluated on data acquired from age-related macular degeneration patients, and "relevance" was defined as the presence of visibly discernible structural abnormalities. The summarisation system consists of a detection module, where relevant B-scans are extracted from the volume, and a set of rules that determines which B-scans are included in the visual summary. Two deep learning approaches are presented and compared for the classification of B-scans-transfer learning and de novo learning. Both approaches performed comparably with AUCs of 0.97 and 0.96, respectively, obtained on an independent test set. The de novo network, however, was 98% smaller than the transfer learning approach, and had a run-time that was also significantly shorter.

Layer boundary evolution method for macular OCT layer segmentation.

Optical coherence tomography (OCT) is used to produce high resolution depth images of the retina and is now the standard of care for in-vivo ophthalmological assessment. It is also increasingly being used for evaluation of neurological disorders such as multiple sclerosis (MS). Automatic segmentation methods identify the retinal layers of the macular cube providing consistent results without intra- and inter-rater variation and is faster than manual segmentation. In this paper, we propose a fast multi-layer macular OCT segmentation method based on a fast level set method. Our framework uses contours in an optimized approach specifically for OCT layer segmentation over the whole macular cube. Our algorithm takes boundary probability maps from a trained random forest and iteratively refines the prediction to subvoxel precision. Evaluation on both healthy and multiple sclerosis subjects shows that our method is statistically better than a state-of-the-art graph-based method.

The Henle Fiber Layer in Albinism: Comparison to Normal and Relationship to Outer Nuclear Layer Thickness and Foveal Cone Density.

Purpose: Directional optical coherence tomography (D-OCT) allows the visualization of the Henle fiber layer (HFL) in vivo. Here, we used D-OCT to characterize the HFL and outer nuclear layer (ONL) in albinism and examine the relationship between true foveal ONL and peak cone density. Methods: Horizontal D-OCT B-scans were acquired, registered, and averaged for 12 subjects with oculocutaneous albinism and 26 control subjects. Averaged images were manually segmented to extract HFL and ONL thickness. Adaptive optics scanning light ophthalmoscopy was used to acquire images of the foveal cone mosaic in 10 subjects with albinism, from which peak cone density was assessed. Results: Across the foveal region, the HFL topography was different between subjects with albinism and normal controls. In particular, foveal HFL thickness was thicker in albinism than in normal controls (P < 0.0001), whereas foveal ONL thickness was thinner in albinism than in normal controls (P < 0.0001). The total HFL and ONL thickness was not significantly different between albinism and controls (P = 0.3169). Foveal ONL thickness was positively correlated with peak cone density in subjects with albinism (r = 0.8061, P = 0.0072). Conclusions: Foveal HFL and ONL topography are significantly altered in albinism relative to normal controls. Our data suggest that increased foveal cone packing drives the formation of Henle fibers, more so than the lateral displacement of inner retinal neurons (which is reduced in albinism). The ability to quantify foveal ONL and HFL may help further stratify grading schemes used to assess foveal hypoplasia.

Retinal optical coherence tomography image enhancement via deep learning.

Optical coherence tomography (OCT) images of the retina are a powerful tool for diagnosing and monitoring eye disease. However, they are plagued by speckle noise, which reduces image quality and reliability of assessment. This paper introduces a novel speckle reduction method inspired by the recent successes of deep learning in medical imaging. We present two versions of the network to reflect the needs and preferences of different end-users. Specifically, we train a convolution neural network to denoise cross-sections from OCT volumes of healthy eyes using either (1) mean-squared error, or (2) a generative adversarial network (GAN) with Wasserstein distance and perceptual similarity. We then interrogate the success of both methods with extensive quantitative and qualitative metrics on cross-sections from both healthy and glaucomatous eyes. The results show that the former approach provides state-of-the-art improvement in quantitative metrics such as PSNR and SSIM, and aids layer segmentation. However, the latter approach, which puts more weight on visual perception, outperformed for qualitative comparisons based on accuracy, clarity, and personal preference. Overall, our results demonstrate the effectiveness and efficiency of a deep learning approach to denoising OCT images, while maintaining subtle details in the images.

Analysis of Agreement of Retinal-Layer Thickness Measures Derived from the Segmentation of Horizontal and Vertical Spectralis OCT Macular Scans.

PURPOSE: Optical coherence tomography (OCT) is a reliable method used to quantify discrete layers of the retina. Spectralis OCT is a device used for this purpose. Spectralis OCT macular scan imaging acquisition can be obtained on either the horizontal or vertical plane. The vertical protocol has been proposed as favorable, due to postulated reduction in confound of Henle's fibers on segmentation-derived metrics. Yet, agreement of the segmentation measures of horizontal and vertical macular scans remains unexplored. Our aim was to determine this agreement. MATERIALS AND METHODS: Horizontal and vertical macular scans on Spectralis OCT were acquired in 20 healthy controls (HCs) and 20 multiple sclerosis (MS) patients. All scans were segmented using Heidelberg software and a Johns Hopkins University (JHU)-developed method. Agreement was analyzed using Bland-Altman analyses and intra-class correlation coefficients (ICCs). RESULTS: Using both segmentation techniques, mean differences (agreement at the cohort level) in the thicknesses of all macular layers derived from both acquisition protocols in MS patients and HCs were narrow (<1 µm), while the limits of agreement (LOA) (agreement at the individual level) were wider. Using JHU segmentation mean differences (and LOA) for the macular retinal nerve fiber layer (RNFL) and ganglion cell layer + inner plexiform layer (GCIP) in MS were 0.21 µm (-1.57-1.99 µm) and -0.36 µm (-1.44-1.37 µm), respectively. CONCLUSIONS: OCT segmentation measures of discrete retinal-layer thicknesses derived from both vertical and horizontal protocols on Spectralis OCT agree excellently at the cohort level (narrow mean differences), but only moderately at the individual level (wide LOA). This suggests patients scanned using either protocol should continue to be scanned with the same protocol. However, due to excellent agreement at the cohort level, measures derived from both acquisitions can be pooled for outcome purposes in clinical trials.

Longitudinal Analysis of Mouse SDOCT Volumes.

Spectral-domain optical coherence tomography (SDOCT), in addition to its routine clinical use in the diagnosis of ocular diseases, has begun to find increasing use in animal studies. Animal models are frequently used to study disease mechanisms as well as to test drug efficacy. In particular, SDOCT provides the ability to study animals longitudinally and non-invasively over long periods of time. However, the lack of anatomical landmarks makes the longitudinal scan acquisition prone to inconsistencies in orientation. Here, we propose a method for the automated registration of mouse SDOCT volumes. The method begins by accurately segmenting the blood vessels and the optic nerve head region in the scans using a pixel classification approach. The segmented vessel maps from follow-up scans were registered using an iterative closest point (ICP) algorithm to the baseline scan to allow for the accurate longitudinal tracking of thickness changes. Eighteen SDOCT volumes from a light damage model study were used to train a random forest utilized in the pixel classification step. The area under the curve (AUC) in a leave-one-out study for the retinal blood vessels and the optic nerve head (ONH) was found to be 0.93 and 0.98, respectively. The complete proposed framework, the retinal vasculature segmentation and the ICP registration, was applied to a secondary set of scans obtained from a light damage model. A qualitative assessment of the registration showed no registration failures.

Collaborative SDOCT Segmentation and Analysis Software.

Spectral domain optical coherence tomography (SDOCT) is routinely used in the management and diagnosis of a variety of ocular diseases. This imaging modality also finds widespread use in research, where quantitative measurements obtained from the images are used to track disease progression. In recent years, the number of available scanners and imaging protocols grown and there is a distinct absence of a unified tool that is capable of visualizing, segmenting, and analyzing the data. This is especially noteworthy in longitudinal studies, where data from older scanners and/or protocols may need to be analyzed. Here, we present a graphical user interface (GUI) that allows users to visualize and analyze SDOCT images obtained from two commonly used scanners. The retinal surfaces in the scans can be segmented using a previously described method, and the retinal layer thicknesses can be compared to a normative database. If necessary, the segmented surfaces can also be corrected and the changes applied. The interface also allows users to import and export retinal layer thickness data to an SQL database, thereby allowing for the collation of data from a number of collaborating sites.

Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model.

The use of spectral-domain optical coherence tomography (SD-OCT) is becoming commonplace for the in vivo longitudinal study of murine models of ophthalmic disease. Longitudinal studies, however, generate large quantities of data, the manual analysis of which is very challenging due to the time-consuming nature of generating delineations. Thus, it is of importance that automated algorithms be developed to facilitate accurate and timely analysis of these large datasets. Furthermore, as the models target a variety of diseases, the associated structural changes can also be extremely disparate. For instance, in the light damage (LD) model, which is frequently used to study photoreceptor degeneration, the outer retina appears dramatically different from the normal retina. To address these concerns, we have developed a flexible graph-based algorithm for the automated segmentation of mouse OCT volumes (ASiMOV). This approach incorporates a machine-learning component that can be easily trained for different disease models. To validate ASiMOV, the automated results were compared to manual delineations obtained from three raters on healthy and BALB/cJ mice post LD. It was also used to study a longitudinal LD model, where five control and five LD mice were imaged at four timepoints post LD. The total retinal thickness and the outer retina (comprising the outer nuclear layer, and inner and outer segments of the photoreceptors) were unchanged the day after the LD, but subsequently thinned significantly (p < 0.01). The retinal nerve fiber-ganglion cell complex and the inner plexiform layers, however, remained unchanged for the duration of the study.

Novel method using 3-dimensional segmentation in spectral domain-optical coherence tomography imaging in the chick reveals defocus-induced regional and time-sensitive asymmetries in the choroidal thickness.

Studies into the mechanisms underlying the active emmetropization process by which neonatal refractive errors are corrected, have described rapid, compensatory changes in the thickness of the choroidal layer in response to imposed optical defocus. While high frequency A-scan ultrasonography, as traditionally used to characterize such changes, offers good resolution of central (on-axis) changes, evidence of local retinal control mechanisms make it imperative that more peripheral, off-axis changes also be tracked. In this study, we used in vivo high resolution spectral domain-optical coherence tomography (SD-OCT) imaging in combination with the Iowa Reference Algorithms for 3-dimensional segmentation, to more fully characterize these changes, both spatially and temporally, in young, 7-day old chicks (n = 15), which were fitted with monocular +15 D defocusing lenses to induce choroidal thickening. With these tools, we were also able to localize the retinal area centralis, which was used as a landmark along with the ocular pectin in standardizing the location of scans and aligning them for subsequent analyses of choroidal thickness (CT) changes across time and between eyes. Values were derived for each of four quadrants, centered on the area centralis, and global CT values were also derived for all eyes. Data were compared with on-axis changes measured using ultrasonography. There were significant on-axis choroidal thickening that was detected after just one day of lens wear (∼190 µm), and regional (quadrant-related) differences in choroidal responses were also found, as well as global thickness changes 1 day after treatment. The ratio of global to on-axis choroidal thicknesses, used as an index of regional variability in responses, was also found to change significantly, reflecting the significant central changes. In summary, we demonstrated in vivo high resolution SD-OCT imaging, used in combination with segmentation algorithms, to be a viable and informative approach for characterizing regional (spatial), time-sensitive changes in CT in small animals such as the chick.