Effects of Myopia and Glaucoma on the Neural Canal and Lamina Cribrosa Using Optical Coherence Tomography.

PRCIS: Glaucoma was associated with axial bowing and rotation of Bruchs membrane opening (BMO) and anterior laminar insertion (ALI), skewed neural canal, and deeper anterior lamina cribrosa surface (ALCS). Longer axial length was associated with wider, longer, and more skewed neural canal and flatter ALCS. PURPOSE: Investigate the effects of myopia and glaucoma in the prelaminar neural canal and anterior lamina cribrosa using 1060-nm swept-source optical coherence tomography. PATIENTS: 19 control (38 eyes) and 38 glaucomatous subjects (63 eyes). MATERIALS AND METHODS: Participants were imaged with swept-source optical coherence tomography, and the images were analyzed for the BMO and ALI dimensions, prelaminar neural canal dimensions, and ALCS depth. RESULTS: Glaucomatous eyes had more bowed and nasally rotated BMO and ALI, more horizontally skewed prelaminar neural canal, and deeper ALCS than the control eyes. Increased axial length was associated with a wider, longer, and more horizontally skewed neural canal and a decrease in the ALCS depth and curvature. CONCLUSION: Our findings suggest that glaucomatous posterior bowing or cupping of lamina cribrosa can be significantly confounded by the myopic expansion of the neural canal. This may be related to higher glaucoma risk associated with myopia from decreased compliance and increased susceptibility to IOP-related damage of LC being pulled taut.

The effect of stiffened diabetic red blood cells on wall shear stress in a reconstructed 3D microaneurysm.

Blood flow within the vasculature of the retina has been found to influence the progression of diabetic retinopathy. In this research cell resolved blood flow simulations are used to study the pulsatile flow of whole blood through a segmented retinal microaneurysm. Images were collected using adaptive optics optical coherence tomography of the retina of a patient with diabetic retinopathy, and a sidewall (sacciform) microaneurysm was segmented from the volumetric data. The original microaneurysm neck width was varied to produce two additional aneurysm geometries in order to probe the influence of neck width on the transport of red blood cells and platelets into the aneurysm. Red blood cell membrane stiffness was also increased to resolve the impact of rigid red blood cells, as a result of diabetes, in blood flow. Wall shear stress and wall shear stress gradients were calculated throughout the aneurysm domains, and the quantification of the influence of the red blood cells is presented. Average wall shear stress and wall shear stress gradients increased due to the increase of red blood cell membrane stiffness. Stiffened red blood cells were also found to induce higher local wall shear stress and wall shear stress gradients as they passed through the leading and draining parental vessels. Stiffened red blood cells were found to penetrate the aneurysm sac more than healthy red blood cells, as well as decreasing the margination of platelets to the vessel walls of the parental vessel, which caused a decrease in platelet penetration into the aneurysm sac.

ADAPTIVE OPTICS OPTICAL COHERENCE TOMOGRAPHY IN A CASE OF ACUTE ZONAL OCCULT OUTER RETINOPATHY.

PURPOSE: To report a case of acute zonal occult outer retinopathy in which adaptive optics (AO) facilitated visualization of abnormal photoreceptors previously thought to be in an area of normal retina on conventional optical coherence tomography (OCT). METHODS: Case report. RESULTS: A 51-year-old woman presents with 11-month history of photopsias and scotoma in the temporal visual field of her left eye. Ocular imaging including fluorescein angiography, fundus autofluorescence and OCT suggested the diagnosis of acute zonal occult outer retinopathy in the left eye. Adaptive optics optical coherence tomography (AO-OCT) revealed photoreceptor abnormalities not previously identified in conventional OCT, in areas apparently normal on multimodal imaging. On enface and cross-sectional AO-OCT, round and evenly spaced hyperreflectivity corresponding to normal cone mosaic (Pattern 1) was adjacent to unevenly and disrupted cone hyperreflectivity (Pattern 2) and areas with hyporeflectivity or no cone reflectivity (Pattern 3). Cross-sectional AO-OCT of Patterns 2 and 3 also revealed attenuation of ellipsoid zone with loss of interdigitation zone. CONCLUSION: Adaptive optics OCT documented cone photoreceptors in finer details than conventional OCT and revealed early changes in a patient with acute zonal occult outer retinopathy, in an area of the retina thought to be normal on conventional multimodal imaging. These findings may provide important insight into pathogenesis and progression of the disease.

LF-UNet - A novel anatomical-aware dual-branch cascaded deep neural network for segmentation of retinal layers and fluid from optical coherence tomography images.

Computer-assistant diagnosis of retinal disease relies heavily on the accurate detection of retinal boundaries and other pathological features such as fluid accumulation. Optical coherence tomography (OCT) is a non-invasive ophthalmological imaging technique that has become a standard modality in the field due to its ability to detect cross-sectional retinal pathologies at the micrometer level. In this work, we presented a novel framework to achieve simultaneous retinal layers and fluid segmentation. A dual-branch deep neural network, termed LF-UNet, was proposed which combines the expansion path of the U-Net and original fully convolutional network, with a dilated network. In addition, we introduced a cascaded network framework to include the anatomical awareness embedded in the volumetric image. Cross validation experiments showed that the proposed LF-UNet has superior performance compared to the state-of-the-art methods, and that incorporating the relative positional map structural prior information could further improve the performance regardless of the network. The generalizability of the proposed network was demonstrated on an independent dataset acquired from the same types of device with different field of view, or images acquired from different device.

Investigation of the Peripapillary Choriocapillaris in Normal Tension Glaucoma, Primary Open-angle Glaucoma, and Control Eyes.

PRECIS: The peripapillary choriocapillaris (CC) was observed to be significantly impaired in normal tension glaucoma (NTG) subjects compared with normal controls using optical coherence tomography angiography (OCTA). PURPOSE: The aim was to quantitatively evaluate the peripapillary CC in NTG, primary open-angle glaucoma (POAG), and control eyes using OCTA. MATERIALS AND METHODS: Ninety eyes (30 controls, 30 NTG, and 30 POAG) from 73 patients were imaged using the Zeiss Plex Elite 9000. Five repeat 3×3 mm OCTA scans were acquired both nasally and temporally to the optic disc and subsequently averaged. Four CC flow deficit (FD) measures were calculated using the fuzzy C-means approach: FD density (FDD), mean FD size (MFDS), FD number (FDN), and FD area (FDA). RESULTS: Temporal NTG CC parameters were associated with visual field index and mean deviation (P<0.05). The control group showed a significantly lower nasal FDD (nasal: 3.79±1.26%, temporal: 4.48±1.73%, P=0.03), FDN (nasal: 156.43±38.44, temporal: 178.40±45.68, P=0.02), and FDA (nasal: 0.22±0.08, temporal: 0.26±0.10, P=0.03) when compared with temporal optic disc. The NTG group showed a significantly higher FDD (NTG: 5.04±2.38%, control: 3.79±1.26%, P=0.03), FDN (NTG: 185.90±56.66, control: 156.43±38.44, P=0.04), and FDA (NTG: 0.30±0.14 mm2, control: 0.22±0.08 mm2, P=0.03) nasal to the optic disc compared with controls. CONCLUSIONS: Association between CC parameters and glaucoma severity in NTG, but not POAG subjects, suggests vascular abnormalities may be a potential factor in the multifactorial process of glaucoma damage in NTG patients.

Optimizing 3D retinal vasculature imaging in diabetic retinopathy using registration and averaging of OCT-A.

High resolution visualization of optical coherence tomography (OCT) and OCT angiography (OCT-A) data is required to fully take advantage of the imaging modality's three-dimensional nature. However, artifacts induced by patient motion often degrade OCT-A data quality. This is especially true for patients with deteriorated focal vision, such as those with diabetic retinopathy (DR). We propose a novel methodology for software-based OCT-A motion correction achieved through serial acquisition, volumetric registration, and averaging. Motion artifacts are removed via a multi-step 3D registration process, and visibility is significantly enhanced through volumetric averaging. We demonstrate that this method permits clear 3D visualization of retinal pathologies and their surrounding features, 3D visualization of inner retinal capillary connections, as well as reliable visualization of the choriocapillaris layer.

Semi-supervised deep learning based 3D analysis of the peripapillary region.

Optical coherence tomography (OCT) has become an essential tool in the evaluation of glaucoma, typically through analyzing retinal nerve fiber layer changes in circumpapillary scans. Three-dimensional OCT volumes enable a much more thorough analysis of the optic nerve head (ONH) region, which may be the site of initial glaucomatous optic nerve damage. Automated analysis of this region is of great interest, though large anatomical variations and the termination of layers make the requisite peripapillary layer and Bruch's membrane opening (BMO) segmentation a challenging task. Several machine learning-based segmentation methods have been proposed for retinal layer segmentation, and a few for the ONH region, but they typically depend on either heavily averaged or pre-processed B-scans or a large amount of annotated data, which is a tedious task and resource-intensive. We evaluated a semi-supervised adversarial deep learning method for segmenting peripapillary retinal layers in OCT B-scans to take advantage of unlabeled data. We show that the use of a generative adversarial network and unlabeled data can improve the performance of segmentation. Additionally, we use a Faster R-CNN architecture to automatically segment the BMO. The proposed methods are then used for the 3D morphometric analysis of both control and glaucomatous ONH volumes to demonstrate the potential for clinical utility.

Ensemble Deep Learning for Diabetic Retinopathy Detection Using Optical Coherence Tomography Angiography.

Purpose: To evaluate the role of ensemble learning techniques with deep learning in classifying diabetic retinopathy (DR) in optical coherence tomography angiography (OCTA) images and their corresponding co-registered structural images. Methods: A total of 463 volumes from 380 eyes were acquired using the 3 × 3-mm OCTA protocol on the Zeiss Plex Elite system. Enface images of the superficial and deep capillary plexus were exported from both the optical coherence tomography and OCTA data. Component neural networks were constructed using single data-types and fine-tuned using VGG19, ResNet50, and DenseNet architectures pretrained on ImageNet weights. These networks were then ensembled using majority soft voting and stacking techniques. Results were compared with a classifier using manually engineered features. Class activation maps (CAMs) were created using the original CAM algorithm and Grad-CAM. Results: The networks trained with the VGG19 architecture outperformed the networks trained on deeper architectures. Ensemble networks constructed using the four fine-tuned VGG19 architectures achieved accuracies of 0.92 and 0.90 for the majority soft voting and stacking methods respectively. Both ensemble methods outperformed the highest single data-type network and the network trained on hand-crafted features. Grad-CAM was shown to more accurately highlight areas of disease. Conclusions: Ensemble learning increases the predictive accuracy of CNNs for classifying referable DR on OCTA datasets. Translational Relevance: Because the diagnostic accuracy of OCTA images is shown to be greater than the manually extracted features currently used in the literature, the proposed methods may be beneficial toward developing clinically valuable solutions for DR diagnoses.

Microvasculature Segmentation and Intercapillary Area Quantification of the Deep Vascular Complex Using Transfer Learning.

Purpose: Optical coherence tomography angiography (OCT-A) permits visualization of the changes to the retinal circulation due to diabetic retinopathy (DR), a microvascular complication of diabetes. We demonstrate accurate segmentation of the vascular morphology for the superficial capillary plexus (SCP) and deep vascular complex (DVC) using a convolutional neural network (CNN) for quantitative analysis. Methods: The main CNN training dataset consisted of retinal OCT-A with a 6 × 6-mm field of view (FOV), acquired using a Zeiss PlexElite. Multiple-volume acquisition and averaging enhanced the vasculature contrast used for constructing the ground truth for neural network training. We used transfer learning from a CNN trained on smaller FOVs of the SCP acquired using different OCT instruments. Quantitative analysis of perfusion was performed on the resulting automated vasculature segmentations in representative patients with DR. Results: The automated segmentations of the OCT-A images maintained the distinct morphologies of the SCP and DVC. The network segmented the SCP with an accuracy and Dice index of 0.8599 and 0.8618, respectively, and 0.7986 and 0.8139, respectively, for the DVC. The inter-rater comparisons for the SCP had an accuracy and Dice index of 0.8300 and 0.6700, respectively, and 0.6874 and 0.7416, respectively, for the DVC. Conclusions: Transfer learning reduces the amount of manually annotated images required while producing high-quality automatic segmentations of the SCP and DVC that exceed inter-rater comparisons. The resulting intercapillary area quantification provides a tool for in-depth clinical analysis of retinal perfusion. Translational Relevance: Accurate retinal microvasculature segmentation with the CNN results in improved perfusion analysis in diabetic retinopathy.

Evaluating Signs of Microangiopathy Secondary to Diabetes in Different Areas of the Retina with Swept Source OCTA.

Purpose: The purpose of this study was to compare perfusion parameters of the parafovea with scans outside the parafovea to find an area most susceptible to changes secondary to diabetic retinopathy (DR). Methods: Patients with different DR severity levels as well as controls were included in this cross-sectional clinical trial. Seven standardized 3 × 3 mm areas were recorded with Swept Source Optical Coherence Tomography Angiography: one centered on the fovea, three were temporal to the fovea, and three nasally to the optic disc. The capillary perfusion density (PD) of the superficial capillary complex (SCC) and deep capillary complex (DCC) as well as the fractal dimension (FD) were generated. Statistical analyses were done with R software. Results: One hundred ninety-two eyes (33 controls, 51 no-DR, 41 mild DR, 37 moderate/severe DR, and 30 proliferative DR), of which 105 patients with diabetes and 25 healthy controls were included (59 ± 15 years; 62 women). Mean PD of the DCC was significantly less in patients without DR (parafovea = 0.48 ± 0.03; temporal = 0.48 ± 0.02; and nasal = 0.48 ± 0.03) compared to controls (parafovea = 0.49 ± 0.02; temporal = 0.50 ± 0.02; and nasal = 0.50 ± 0.03). With increasing DR severity, PD and FD of the SCC and DCC further decreased. Conclusions: Capillary perfusion of the retina is affected early by diabetes. PD of the DCC was significantly reduced in patients with diabetes who did not have any clinical signs of DR. The capillary network outside the parafovea was more susceptible to capillary perfusion deficits compared to the capillaries close to the fovea. Trial Registration: clinicaltrial.gov, NCT03765112, https://clinicaltrials.gov/ct2/show/NCT03765112?term=NCT03765112&rank=1.

EN FACE OPTICAL COHERENCE TOMOGRAPHY AND OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY OF INNER RETINAL DIMPLES AFTER INTERNAL LIMITING MEMBRANE PEELING FOR FULL-THICKNESS MACULAR HOLES.

PURPOSE: To quantitatively and qualitatively evaluate the microvascular and structural abnormalities associated with inner retinal dimpling after internal limiting membrane peeling for full-thickness macular holes using sequential en face optical coherence tomography (OCT) and OCT angiography. METHODS: Thirteen eyes of 13 patients with idiopathic full-thickness macular holes were enrolled in the study. Patients were treated with pars plana vitrectomy, internal limiting membrane peeling, and gas tamponade. Subjects were evaluated preoperatively and at postoperative Months 1, 3, and 6. At each visit, patients underwent a comprehensive ophthalmologic examination, en face OCT and OCT angiography. The morphology and number and proportionate area of inner retinal dimples were analyzed. Vessel density of the superficial vascular complex at all visits was also measured. RESULTS: Inner retinal dimples were identified 1 month after surgery in all cases. The number and proportionate area of inner retinal dimples significantly increased over the follow-up period (P = 0.05). Preoperative vessel density of the superficial vascular complex was 17.9 ± 1.9 and did not change significantly over the follow-up period (P = 0.15). CONCLUSION: Inner retinal dimples are identified with en face OCT as early as the first month after internal limiting membrane peeling for idiopathic full-thickness macular holes and progressively increase in number and proportionate area in the subsequent 3 to 6 months after surgery. This may be the result of progressive deturgescence of the nerve fiber layer in the postoperative period.

Application of optical coherence tomography angiography in diabetic retinopathy: a comprehensive review.

Optical coherence tomography angiography (OCTA) is a noninvasive method that enables visualization of blood flow within retinal vessels down to the size of capillaries by detecting motion contrast from moving blood cells. OCTA provides a fast and safe procedure to assess retinal microvasculature with higher contrast and resolution than conventional fluorescence angiography. The different capillary plexuses are displayed separately and their perfusion density can be quantified. Imaging capabilities such as these have led to an emerging field of clinical application for OCTA in vascular diseases such as diabetic retinopathy (DR). Evaluation of parameters such as parafoveal capillary perfusion density could be a biomarker for disease diagnosis and progression. Typical microvascular changes in DR such as capillary nonperfusion, microaneurysms, intraretinal microvascular abnormalities, and neovascularization can be reliably detected in optical coherence tomography angiograms, characterized in detail and attributed to the different capillary plexuses. Monitoring of these lesions in vivo gives potential novel insight into the pathophysiology in DR. The aim of this article is to summarize the potential applications/utility of OCTA in DR reported in the literature.

Longitudinal Analysis of Bruch Membrane Opening Morphometry in Myopic Glaucoma.

PRéCIS:: The Bruch membrane opening (BMO) was posteriorly bowed and the degree of nonplanarity increased in stable and progressive glaucoma subjects. BMO became more posterior relative to the Bruch membrane (BM) in control and both stable and progressive glaucoma subjects. PURPOSE: To investigate longitudinal changes in morphologic characteristics of the BMO in control and glaucomatous subjects. MATERIALS AND METHODS: A total of 53 myopic eyes (17 control, 6 suspect, 20 stable glaucoma, and 10 progressing glaucoma) were followed for an average of 4.2±1.4 years and imaged at the baseline and 2 follow-up appointments using a 1060 nm swept-source optical coherence tomography system. BM and BMO were segmented, and 4 morphometric BMO parameters (area, ellipse ratio, nonplanarity, and depth) were measured. RESULTS: There were no significant changes in BMO area or ellipse ratio for all groups. BMO nonplanarity was shown to increase in the glaucoma groups. BMO depth relative to BM increased in all groups except the suspects (control: 8.1 µm/y, P=0.0001; stable glaucoma: 3.5 µm/y, P=0.0001; progressing glaucoma: 14.0 µm/y, P=0.0026). In linear mixed-model analysis, axial length was positively associated with BMO area in all groups except for progressing glaucoma, and with BMO nonplanarity in stable glaucoma. It was not a significant factor to the slopes of the BMO parameters in the ANCOVA analysis of slopes. CONCLUSIONS: Longitudinally, BMO increased in nonplanarity in the glaucoma eyes, and its axial position relative to BM became more posterior in both control and glaucoma eyes.

Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network.

As a non-invasive imaging modality, optical coherence tomography (OCT) can provide micrometer-resolution 3D images of retinal structures. These images can help reveal disease-related alterations below the surface of the retina, such as the presence of edema, or accumulation of fluid which can distort vision, and are an indication of disruptions in the vasculature of the retina. In this paper, a new framework is proposed for multiclass fluid segmentation and detection in the retinal OCT images. Based on the intensity of OCT images and retinal layer segmentations provided by a graph-cut algorithm, a fully convolutional neural network was trained to recognize and label the fluid pixels. Random forest classification was performed on the segmented fluid regions to detect and reject the falsely labeled fluid regions. The proposed framework won the first place in the MICCAI RETOUCH challenge in 2017 on both the segmentation performance (mean Dice: 0.7667) and the detection performance (mean AUC: 1.00) tasks.

Automated identification of cone photoreceptors in adaptive optics optical coherence tomography images using transfer learning.

Automated measurements of the human cone mosaic requires the identification of individual cone photoreceptors. The current gold standard, manual labeling, is a tedious process and can not be done in a clinically useful timeframe. As such, we present an automated algorithm for identifying cone photoreceptors in adaptive optics optical coherence tomography (AO-OCT) images. Our approach fine-tunes a pre-trained convolutional neural network originally trained on AO scanning laser ophthalmoscope (AO-SLO) images, to work on previously unseen data from a different imaging modality. On average, the automated method correctly identified 94% of manually labeled cones when compared to manual raters, from twenty different AO-OCT images acquired from five normal subjects. Voronoi analysis confirmed the general hexagonal-packing structure of the cone mosaic as well as the general cone density variability across portions of the retina. The consistency of our measurements demonstrates the high reliability and practical utility of having an automated solution to this problem.

Label-free volumetric imaging of conjunctival collecting lymphatics ex vivo by optical coherence tomography lymphangiography.

We employ optical coherence tomography (OCT) and optical coherence microscopy (OCM) to study conjunctival lymphatics in porcine eyes ex vivo. This study is a precursor to the development of in vivo imaging of the collecting lymphatics for potentially guiding and monitoring glaucoma filtration surgery. OCT scans at 1300 nm and higher-resolution OCM scans at 785 nm reveal the lymphatic vessels via their optical transparency. Equivalent signal characteristics are also observed from blood vessels largely free of blood (and devoid of flow) in the ex vivo conjunctiva. In our lymphangiography, vessel networks were segmented by compensating the depth attenuation in the volumetric OCT/OCM signal, projecting the minimum intensity in two dimensions and thresholding to generate a three-dimensional vessel volume. Vessel segmentation from multiple locations of a range of porcine eyes (n = 21) enables visualization of the vessel networks and indicates the varying spatial distribution of patent lymphatics. Such visualization provides a new tool to investigate conjunctival vessels in tissue ex vivo without need for histological tissue processing and a valuable reference on vessel morphology for the in vivo label-free imaging studies of lymphatics to follow.

Effective bidirectional scanning pattern for optical coherence tomography angiography.

We demonstrate the utility of a novel scanning method for optical coherence tomography angiography (OCTA). Although raster scanning is commonly used for OCTA imaging, a bidirectional approach would lessen the distortion caused by galvanometer-based scanners as sources continue to increase sweep rates. As shown, a unidirectional raster scan approach has a lower effective scanning time than bidirectional approaches; however, a strictly bidirectional approach causes contrast variation along the B-scan direction due to the non-uniform time interval between B-scans. Therefore, a stepped bidirectional approach is introduced and successfully applied to retinal imaging in normal controls and in a pathological subject with diabetic retinopathy.

Quantitative comparisons between optical coherence tomography angiography and matched histology in the human eye.

The aim was to quantitatively compare retinal vascular detail as seen on optical coherence tomography angiography (OCTA) and matched histology in the human eye. 13 normal human donor eyes were used. The central retinal artery was cannulated after which human packed red blood cells were perfused through the retinal vasculature. Retinal vessels were imaged using a custom-built OCTA device during red blood cell perfusion. The eye was subsequently perfused with endothelial cell antibodies and the flat-mounted retina studied histologically using a confocal scanning laser microscope. Qualitative and quantitative comparisons of retinal vascular information as seen on OCTA and histology from the same region of interest were performed. Gradable OCTA images were acquired from 4 of 13 eyes with mean postmortem-to-OCTA imaging time of 4.5 ±â€¯1.3 h 23 pairs of OCTA-histology matched images were evaluated. The retinal arteries and veins had similar pixel intensity on OCTA images. The diameter of retinal veins was significantly greater than its paired artery on OCTA (P < 0.001). The density of vascular structures on OCTA (40.2% ±â€¯10.1%) was significantly less than matched histology (52.1% ±â€¯9.3%, P < 0.001). Mean capillary diameter on OCTA (10.2 ±â€¯2.4 µm) was significantly greater than histology (8.2 ±â€¯2.4 µm; P < 0.001). This is the first study to directly compare OCTA against histology from the same human eye. OCTA visualizes many of the vascular structures in the human retinal circulation but does not exactly match what is seen on histologic examination.

Multiscale sensorless adaptive optics OCT angiography system for in vivo human retinal imaging.

We present a multiscale sensorless adaptive optics (SAO) OCT system capable of imaging retinal structure and vasculature with various fields-of-view (FOV) and resolutions. Using a single deformable mirror and exploiting the polarization properties of light, the SAO-OCT-A was implemented in a compact and easy to operate system. With the ability to adjust the beam diameter at the pupil, retinal imaging was demonstrated at two different numerical apertures with the same system. The general morphological structure and retinal vasculature could be observed with a few tens of micrometer-scale lateral resolution with conventional OCT and OCT-A scanning protocols with a 1.7-mm-diameter beam incident at the pupil and a large FOV (15 deg× 15 deg). Changing the system to a higher numerical aperture with a 5.0-mm-diameter beam incident at the pupil and the SAO aberration correction, the FOV was reduced to 3 deg× 3 deg for fine detailed imaging of morphological structure and microvasculature such as the photoreceptor mosaic and capillaries. Multiscale functional SAO-OCT imaging was performed on four healthy subjects, demonstrating its functionality and potential for clinical utility.

Age and Glaucoma-Related Characteristics in Retinal Nerve Fiber Layer and Choroid: Localized Morphometrics and Visualization Using Functional Shapes Registration.

Optical coherence tomography provides high-resolution 3D imaging of the posterior segment of the eye. However, quantitative morphological analysis, particularly relevant in retinal degenerative diseases such as glaucoma, has been confined to simple sectorization and averaging with limited spatial sensitivity for detection of clinical markers. In this paper, we present point-wise analysis and visualization of the retinal nerve fiber layer and choroid from cross-sectional data using functional shapes (fshape) registration. The fshape framework matches two retinas, or generates a mean of multiple retinas, by jointly optimizing the surface geometry and functional signals mapped on the surface. We generated group-wise mean retinal nerve fiber layer and choroidal surfaces with the respective layer thickness mapping and showed the difference by age (normal, younger vs. older) and by disease (age-matched older, normal vs. glaucomatous) in the two layers, along with a more conventional sector-based analysis for comparison. The fshape results visualized the detailed spatial patterns of the differences between the age-matched normal and glaucomatous retinal nerve fiber layers, with the glaucomatous layers most significantly thinner in the inferior region close to Bruch's membrane opening. Between the young and older normal cases, choroid was shown to be significantly thinner in the older subjects across all regions, but particularly in the nasal and inferior regions. The results demonstrate a comprehensive and detailed analysis with visualization of morphometric patterns by multiple factors.