Segmentation of the optic disc in 3-D OCT scans of the optic nerve head

Characterizing Bruch's membrane: State-of-the-art imaging, computational segmentation, and biologic models in retinal disease and health

The Bruch's membrane (BM) is an acellular, extracellular matrix that lies between the choroid and retinal pigment epithelium (RPE). The BM plays a critical role in retinal health, performing various functions including biomolecule diffusion and RPE support. The BM is also involved in many retinal diseases, and insights into BM dysfunction allow for further understanding of the pathophysiology of various chorioretinal pathologies. Thus, characterization of the BM serves as an important area of research to further understand its involvement in retinal disease. In this article, we provide a review of various advancements in characterizing and visualizing the BM. We provide an overview of the BM in retinal health, as well as changes observed in aging and disease. We then describe current state-of-the-art imaging modalities and advances to further visualize the BM including various types of optical coherence tomography imaging, near-infrared reflectance (NIR), and autofluorescence imaging and tissue matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS). Following advances in imaging of the BM, we describe animal, cellular, and synthetic models that have been developed to further visualize the BM. Following this section, we provide an overview of deep learning in retinal imaging and describe advances in computational and artificial intelligence (AI) techniques to provide automated segmentation of the BM and BM opening. We conclude this section considering the clinical implications of these segmentation techniques. Ultimately, the diverse advances aimed to further characterize the BM may allow for deeper insights into the involvement of this critical structure in retinal health and disease.

Influence of drusenoid pigment epithelial detachments on the progression of age-related macular degeneration and visual acuity

OBJECTIVE

To analyze the presence and morphologic characteristics of drusenoid pigment epithelial detachments (DPEDs) in spectral-domain optical coherence tomography (SD-OCT) in Caucasian patients with early and intermediate age-related macular degeneration (AMD) as well as the influence of these characteristics on best-corrected visual acuity (BCVA) and disease progression.

DESIGN

Prospective observational cohort study.

PARTICIPANTS

89 eyes of 56 patients with early and intermediate AMD.

METHODS

Examinations consisted of BCVA, SD-OCT, and indocyanine green angiography. Evaluated parameters included drusen type, mean drusen height and -volume, the presence of DPED, DPED maximum height, -maximum diameter, -volume, topographic location, the rate of DPED collapse, and the development of macular neovascularization (MNV) or geographic atrophy (GA).

RESULTS

DPED maximum height (162.34 µm ± 75.70 μm, p = 0.019) was significantly associated with the development of GA and MNV. For each additional 100 μm in maximum height, the odds of developing any late AMD (GA or MNV) increased by 2.23 (95% CI = 1.14-4.35). The presence of DPED (44 eyes, p = 0.01), its volume (0.20 mm ± 0.20 mm, p = 0.01), maximum diameter (1860.87 μm ± 880.74 μm, p = 0.03), maximum height (p < 0.001) and topographical location in the central millimetre (p = 0.004) of the Early Treatment Diabetic Retinopathy Study (ETDRS)-Grid were significantly correlated with BCVA at the last follow-up (0.15logMAR ± 0.20logMAR; Snellen equivalent approximately 20/28). DPEDs occurred significantly less in the outer quadrants than in the central millimetre and inner quadrants of ETDRS-Grid (all p values < 0.001).

CONCLUSIONS

The height of drusen and DPEDs is a biomarker that is significantly associated with the development of late AMD and visual loss. DPEDs affect predominantly the center and inner quadrants of the ETDRS-Grid.

Hybrid deep learning and optimal graph search method for optical coherence tomography layer segmentation in diseases affecting the optic nerve

Accurate segmentation of retinal layers in optical coherence tomography (OCT) images is critical for assessing diseases that affect the optic nerve, but existing automated algorithms often fail when pathology causes irregular layer topology, such as extreme thinning of the ganglion cell-inner plexiform layer (GCIPL). Deep LOGISMOS, a hybrid approach that combines the strengths of deep learning and 3D graph search to overcome their limitations, was developed to improve the accuracy, robustness and generalizability of retinal layer segmentation. The method was trained on 124 OCT volumes from both eyes of 31 non-arteritic anterior ischemic optic neuropathy (NAION) patients and tested on three cross-sectional datasets with available reference tracings: Test-NAION (40 volumes from both eyes of 20 NAION subjects), Test-G (29 volumes from 29 glaucoma subjects/eyes), and Test-JHU (35 volumes from 21 multiple sclerosis and 14 control subjects/eyes) and one longitudinal dataset without reference tracings: Test-G-L (155 volumes from 15 glaucoma patients/eyes). In the three test datasets with reference tracings (Test-NAION, Test-G, and Test-JHU), Deep LOGISMOS achieved very high Dice similarity coefficients (%) on GCIPL: 89.97±3.59, 90.63±2.56, and 94.06±1.76, respectively. In the same context, Deep LOGISMOS outperformed the Iowa reference algorithms by improving the Dice score by 17.5, 5.4, and 7.5, and also surpassed the deep learning framework nnU-Net with improvements of 4.4, 3.7, and 1.0. For the 15 severe glaucoma eyes with marked GCIPL thinning (Test-G-L), it demonstrated reliable regional GCIPL thickness measurement over five years. The proposed Deep LOGISMOS approach has potential to enhance precise quantification of retinal structures, aiding diagnosis and treatment management of optic nerve diseases.

Automatic Segmentation of the Optic Nerve Head Region in Optical Coherence Tomography: A Methodological Review

The optic nerve head (ONH) represents the intraocular section of the optic nerve, which is prone to damage by intraocular pressure (IOP). The advent of optical coherence tomography (OCT) has enabled the evaluation of novel ONH parameters, namely the depth and curvature of the lamina cribrosa (LC). Together with the Bruch's membrane minimum-rim-width (BMO-MRW), these seem to be promising ONH parameters for diagnosis and monitoring of retinal diseases such as glaucoma. Nonetheless, these OCT derived biomarkers are mostly extracted through manual segmentation, which is time-consuming and prone to bias, thus limiting their usability in clinical practice. The automatic segmentation of ONH in OCT scans could further improve the current clinical management of glaucoma and other diseases. This review summarizes the current state-of-the-art in automatic segmentation of the ONH in OCT. PubMed and Scopus were used to perform a systematic review. Additional works from other databases (IEEE, Google Scholar and ARVO IOVS) were also included, resulting in a total of 29 reviewed studies. For each algorithm, the methods, the size and type of dataset used for validation, and the respective results were carefully analysed. The results show a lack of consensus regarding the definition of segmented regions, extracted parameters and validation approaches, highlighting the importance and need of standardized methodologies for ONH segmentation. Only with a concrete set of guidelines, these automatic segmentation algorithms will build trust in data-driven segmentation models and be able to enter clinical practice.

Performance evaluation of various deep learning based models for effective glaucoma evaluation using optical coherence tomography images

Glaucoma is the dominant reason for irreversible blindness worldwide, and its best remedy is early and timely detection. Optical coherence tomography has come to be the most commonly used imaging modality in detecting glaucomatous damage in recent years. Deep Learning using Optical Coherence Tomography Modality helps in predicting glaucoma more accurately and less tediously. This experimental study aims to perform glaucoma prediction using eight different ImageNet models from Optical Coherence Tomography of Glaucoma. A thorough investigation is performed to evaluate these models' performances on various efficiency metrics, which will help discover the best performing model. Every net is tested on three different optimizers, namely Adam, Root Mean Squared Propagation, and Stochastic Gradient Descent, to find the best relevant results. An attempt has been made to improvise the performance of models using transfer learning and fine-tuning. The work presented in this study was initially trained and tested on a private database that consists of 4220 images (2110 normal optical coherence tomography and 2110 glaucoma optical coherence tomography). Based on the results, the four best-performing models are shortlisted. Later, these models are tested on the well-recognized standard public Mendeley dataset. Experimental results illustrate that VGG16 using the Root Mean Squared Propagation Optimizer attains auspicious performance with 95.68% accuracy. The proposed work concludes that different ImageNet models are a good alternative as a computer-based automatic glaucoma screening system. This fully automated system has a lot of potential to tell the difference between normal Optical Coherence Tomography and glaucomatous Optical Coherence Tomography automatically. The proposed system helps in efficiently detecting this retinal infection in suspected patients for better diagnosis to avoid vision loss and also decreases senior ophthalmologists' (experts) precious time and involvement.

Deep Learning-Based Retinal Nerve Fiber Layer Thickness Measurement of Murine Eyes

Purpose

To design a robust and automated estimation method for measuring the retinal nerve fiber layer (RNFL) thickness using spectral domain optical coherence tomography (SD-OCT).

Methods

We developed a deep learning-based image segmentation network for automated segmentation of the RNFL in SD-OCT B-scans of mouse eyes. In total, 5500 SD-OCT B-scans (5200 B-scans were used as training data with the remaining 300 B-scans used as testing data) were used to develop this segmentation network. Postprocessing operations were then applied on the segmentation results to fill any discontinuities or remove any speckles in the RNFL. Subsequently, a three-dimensional retina thickness map was generated by z-stacking 100 segmentation processed thickness B-scan images together. Finally, the average absolute difference between algorithm predicted RNFL thickness compared to the ground truth manual human segmentation was calculated.

Results

The proposed method achieves an average dice similarity coefficient of 0.929 in the SD-OCT segmentation task and an average absolute difference of 0.0009 mm in thickness estimation task on the basis of the testing dataset. We also evaluated our segmentation algorithm on another biological dataset with SD-OCT volumes for RNFL thickness after the optic nerve crush injury. Results were shown to be comparable between the predicted and manually measured retina thickness values.

Conclusions

Experimental results demonstrate that our automated segmentation algorithm reliably predicts the RNFL thickness in SD-OCT volumes of mouse eyes compared to laborious and more subjective manual SD-OCT RNFL segmentation.

Translational Relevance

Automated segmentation using a deep learning-based algorithm for murine eye OCT effectively and rapidly produced nerve fiber layer thicknesses comparable to manual segmentation.

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.

OCTRexpert:A Feature-based 3D Registration Method for Retinal OCT Images

Medical image registration can be used for studying longitudinal and cross-sectional data, quantitatively monitoring disease progression and guiding computer assisted diagnosis and treatments. However, deformable registration which enables more precise and quantitative comparison has not been well developed for retinal optical coherence tomography (OCT) images. This paper proposes a new 3D registration approach for retinal OCT data called OCTRexpert. To the best of our knowledge, the proposed algorithm is the first full 3D registration approach for retinal OCT images which can be applied to longitudinal OCT images for both normal and serious pathological subjects. In this approach, a pre-processing method is first performed to remove eye motion artifact and then a novel design-detection-deformation strategy is applied for the registration. In the design step, a couple of features are designed for each voxel in the image. In the detection step, active voxels are selected and the point-to-point correspondences between the subject and template images are established. In the deformation step, the image is hierarchically deformed according to the detected correspondences in multi-resolution. The proposed method is evaluated on a dataset with longitudinal OCT images from 20 healthy subjects and 4 subjects diagnosed with serious Choroidal Neovascularization (CNV). Experimental results show that the proposed registration algorithm consistently yields statistically significant improvements in both Dice similarity coefficient and the average unsigned surface error compared with the other registration methods.

Accurate tissue interface segmentation via adversarial pre-segmentation of anterior segment OCT images

Optical Coherence Tomography (OCT) is an imaging modality that has been widely adopted for visualizing corneal, retinal and limbal tissue structure with micron resolution. It can be used to diagnose pathological conditions of the eye, and for developing pre-operative surgical plans. In contrast to the posterior retina, imaging the anterior tissue structures, such as the limbus and cornea, results in B-scans that exhibit increased speckle noise patterns and imaging artifacts. These artifacts, such as shadowing and specularity, pose a challenge during the analysis of the acquired volumes as they substantially obfuscate the location of tissue interfaces. To deal with the artifacts and speckle noise patterns and accurately segment the shallowest tissue interface, we propose a cascaded neural network framework, which comprises of a conditional Generative Adversarial Network (cGAN) and a Tissue Interface Segmentation Network (TISN). The cGAN pre-segments OCT B-scans by removing undesired specular artifacts and speckle noise patterns just above the shallowest tissue interface, and the TISN combines the original OCT image with the pre-segmentation to segment the shallowest interface. We show the applicability of the cascaded framework to corneal datasets, demonstrate that it precisely segments the shallowest corneal interface, and also show its generalization capacity to limbal datasets. We also propose a hybrid framework, wherein the cGAN pre-segmentation is passed to a traditional image analysis-based segmentation algorithm, and describe the improved segmentation performance. To the best of our knowledge, this is the first approach to remove severe specular artifacts and speckle noise patterns (prior to the shallowest interface) that affects the interpretation of anterior segment OCT datasets, thereby resulting in the accurate segmentation of the shallowest tissue interface. To the best of our knowledge, this is the first work to show the potential of incorporating a cGAN into larger deep learning frameworks for improved corneal and limbal OCT image segmentation. Our cGAN design directly improves the visualization of corneal and limbal OCT images from OCT scanners, and improves the performance of current OCT segmentation algorithms.

Automated segmentation of peripapillary retinal boundaries in OCT combining a convolutional neural network and a multi-weights graph search

Quantitative analysis of the peripapillary retinal layers and capillary plexuses from optical coherence tomography (OCT) and OCT angiography images depend on two segmentation tasks - delineating the boundary of the optic disc and delineating the boundaries between retinal layers. Here, we present a method combining a neural network and graph search to perform these two tasks. A comparison of this novel method's segmentation of the disc boundary showed good agreement with the ground truth, achieving an overall Dice similarity coefficient of 0.91 ± 0.04 in healthy and glaucomatous eyes. The absolute error of retinal layer boundaries segmentation in the same cases was 4.10 ± 1.25 µm.

Robust segmentation of retinal layers in optical coherence tomography images based on a multistage active contour model

Optical Coherence Tomography (OCT) constitutes an imaging technique that is increasing its popularity in the ophthalmology field, since it offers a more complete set of information about the main retinal structures. Hence, it offers detailed information about the eye fundus morphology, allowing the identification of many intraretinal pathological signs. For that reason, over the recent years, Computer-Aided Diagnosis (CAD) systems have spread to work with this image modality and analyze its information. A crucial step for the analysis of the retinal tissues implies the identification and delimitation of the different retinal layers. In this context, we present in this work a fully automatic method for the identification of the main retinal layers that delimits the retinal region. Thus, an active contour-based model was completely adapted and optimized to segment these main retinal boundaries. This fully automatic method uses the information of the horizontal placement of these retinal layers and their relative location over the analyzed images to restrict the search space, considering the presence of shadows that are normally generated by pathological or non-pathological artifacts. The validation process was done using the groundtruth of an expert ophthalmologist analyzing healthy as well as unhealthy patients with different degrees of diabetic retinopathy (without macular edema, with macular edema and with lesions in the photoreceptor layers). Quantitative results are in line with the state of the art of this domain, providing accurate segmentations of the retinal layers even when significative pathological alterations are present in the eye fundus. Therefore, the proposed method is robust enough to be used in complex environments, making it feasible for the ophthalmologists in their routine clinical practice.

Optimal surface segmentation with convex priors in irregularly sampled space

Optimal surface segmentation is a state-of-the-art method used for segmentation of multiple globally optimal surfaces in volumetric datasets. The method is widely used in numerous medical image segmentation applications. However, nodes in the graph based optimal surface segmentation method typically encode uniformly distributed orthogonal voxels of the volume. Thus the segmentation cannot attain an accuracy greater than a single unit voxel, i.e. the distance between two adjoining nodes in graph space. Segmentation accuracy higher than a unit voxel is achievable by exploiting partial volume information in the voxels which shall result in non-equidistant spacing between adjoining graph nodes. This paper reports a generalized graph based multiple surface segmentation method with convex priors which can optimally segment the target surfaces in an irregularly sampled space. The proposed method allows non-equidistant spacing between the adjoining graph nodes to achieve subvoxel segmentation accuracy by utilizing the partial volume information in the voxels. The partial volume information in the voxels is exploited by computing a displacement field from the original volume data to identify the subvoxel-accurate centers within each voxel resulting in non-equidistant spacing between the adjoining graph nodes. The smoothness of each surface modeled as a convex constraint governs the connectivity and regularity of the surface. We employ an edge-based graph representation to incorporate the necessary constraints and the globally optimal solution is obtained by computing a minimum s-t cut. The proposed method was validated on 10 intravascular multi-frame ultrasound image datasets for subvoxel segmentation accuracy. In all cases, the approach yielded highly accurate results. Our approach can be readily extended to higher-dimensional segmentations.

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.

Active contour method for ILM segmentation in ONH volume scans in retinal OCT

The optic nerve head (ONH) is affected by many neurodegenerative and autoimmune inflammatory conditions. Optical coherence tomography can acquire high-resolution 3D ONH scans. However, the ONH's complex anatomy and pathology make image segmentation challenging. This paper proposes a robust approach to segment the inner limiting membrane (ILM) in ONH volume scans based on an active contour method of Chan-Vese type, which can work in challenging topological structures. A local intensity fitting energy is added in order to handle very inhomogeneous image intensities. A suitable boundary potential is introduced to avoid structures belonging to outer retinal layers being detected as part of the segmentation. The average intensities in the inner and outer region are then rescaled locally to account for different brightness values occurring among the ONH center. The appropriate values for the parameters used in the complex computational model are found using an optimization based on the differential evolution algorithm. The evaluation of results showed that the proposed framework significantly improved segmentation results compared to the commercial solution.

Multiple surface segmentation using convolution neural nets: application to retinal layer segmentation in OCT images

Automated segmentation of object boundaries or surfaces is crucial for quantitative image analysis in numerous biomedical applications. For example, retinal surfaces in optical coherence tomography (OCT) images play a vital role in the diagnosis and management of retinal diseases. Recently, graph based surface segmentation and contour modeling have been developed and optimized for various surface segmentation tasks. These methods require expertly designed, application specific transforms, including cost functions, constraints and model parameters. However, deep learning based methods are able to directly learn the model and features from training data. In this paper, we propose a convolutional neural network (CNN) based framework to segment multiple surfaces simultaneously. We demonstrate the application of the proposed method by training a single CNN to segment three retinal surfaces in two types of OCT images - normal retinas and retinas affected by intermediate age-related macular degeneration (AMD). The trained network directly infers the segmentations for each B-scan in one pass. The proposed method was validated on 50 retinal OCT volumes (3000 B-scans) including 25 normal and 25 intermediate AMD subjects. Our experiment demonstrated statistically significant improvement of segmentation accuracy compared to the optimal surface segmentation method with convex priors (OSCS) and two deep learning based UNET methods for both types of data. The average computation time for segmenting an entire OCT volume (consisting of 60 B-scans each) for the proposed method was 12.3 seconds, demonstrating low computation costs and higher performance compared to the graph based optimal surface segmentation and UNET based methods.

Development of an efficient algorithm for the detection of macular edema from optical coherence tomography images

PURPOSE

Detection of eye diseases and their treatment is a key to reduce blindness, which impacts human daily needs like driving, reading, writing, etc. Several methods based on image processing have been used to monitor the presence of macular diseases. Optical coherence tomography (OCT) imaging is the most efficient technique used to observe eye diseases. This paper proposes an efficient algorithm to automatically classify normal as well as disease-affected (macular edema) retinal OCT images by using segmentation of Inner Limiting Membrane and the Choroid Layer.

METHODS

In the proposed method, preprocessing of the input image is done to improve the quality and reduce the speckle noise. The layer segmentation is done on the gradient image, and graph theory and dynamic programming algorithm is performed. The feature vectors from segmented image are in terms of thickness profile and cyst fluid parameter, and these features are applied to various classifiers.

RESULTS

The proposed method was tested with the standard dataset collected from the Department of Ophthalmology, Duke University, and achieved a high accuracy rate of 99.4975%, sensitivity of 100%, and specificity of 99% for the SVM classifier.

CONCLUSIONS

An efficient algorithm is proposed for macular edema detection from OCT images using segmentation based on graph theory and dynamic programming algorithm. The comparison with alternative methods yielded results that demonstrate the superiority of the proposed algorithm for macular edema detection.

Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation

Glaucoma is a chronic eye disease that leads to irreversible vision loss. The cup to disc ratio (CDR) plays an important role in the screening and diagnosis of glaucoma. Thus, the accurate and automatic segmentation of optic disc (OD) and optic cup (OC) from fundus images is a fundamental task. Most existing methods segment them separately, and rely on hand-crafted visual feature from fundus images. In this paper, we propose a deep learning architecture, named M-Net, which solves the OD and OC segmentation jointly in a one-stage multi-label system. The proposed M-Net mainly consists of multi-scale input layer, U-shape convolutional network, side-output layer, and multi-label loss function. The multi-scale input layer constructs an image pyramid to achieve multiple level receptive field sizes. The U-shape convolutional network is employed as the main body network structure to learn the rich hierarchical representation, while the side-output layer acts as an early classifier that produces a companion local prediction map for different scale layers. Finally, a multi-label loss function is proposed to generate the final segmentation map. For improving the segmentation performance further, we also introduce the polar transformation, which provides the representation of the original image in the polar coordinate system. The experiments show that our M-Net system achieves state-of-the-art OD and OC segmentation result on ORIGA data set. Simultaneously, the proposed method also obtains the satisfactory glaucoma screening performances with calculated CDR value on both ORIGA and SCES datasets.

MULTI-SCALE SEGMENTATION USING DEEP GRAPH CUTS: ROBUST LUNG TUMOR DELINEATION IN MVCBCT

Deep networks have been used in a growing trend in medical image analysis with the remarkable progress in deep learning. In this paper, we formulate the multi-scale segmentation as a Markov Random Field (MRF) energy minimization problem in a deep network (graph), which can be efficiently and exactly solved by computing a minimum s-t cut in an appropriately constructed graph. The performance of the proposed method is assessed on the application of lung tumor segmentation in 38 mega-voltage cone-beam computed tomography datasets.

Shared-hole graph search with adaptive constraints for 3D optic nerve head optical coherence tomography image segmentation

Optic nerve head (ONH) is a crucial region for glaucoma detection and tracking based on spectral domain optical coherence tomography (SD-OCT) images. In this region, the existence of a "hole" structure makes retinal layer segmentation and analysis very challenging. To improve retinal layer segmentation, we propose a 3D method for ONH centered SD-OCT image segmentation, which is based on a modified graph search algorithm with a shared-hole and locally adaptive constraints. With the proposed method, both the optic disc boundary and nine retinal surfaces can be accurately segmented in SD-OCT images. An overall mean unsigned border positioning error of 7.27 ± 5.40 µm was achieved for layer segmentation, and a mean Dice coefficient of 0.925 ± 0.03 was achieved for optic disc region detection.

A Survey of Graph Cuts/Graph Search Based Medical Image Segmentation

Medical image segmentation is a fundamental and challenging problem for analyzing medical images. Among different existing medical image segmentation methods, graph-based approaches are relatively new and show good features in clinical applications. In the graph-based method, pixels or regions in the original image are interpreted into nodes in a graph. By considering Markov random field to model the contexture information of the image, the medical image segmentation problem can be transformed into a graph-based energy minimization problem. This problem can be solved by the use of minimum s-t cut/ maximum flow algorithm. This review is devoted to cut-based medical segmentation methods, including graph cuts and graph search for region and surface segmentation. Different varieties of cut-based methods, including graph-cuts-based methods, model integrated graph cuts methods, graph-search-based methods, and graph search/graph cuts based methods, are systematically reviewed. Graph cuts and graph search with deep learning technique are also discussed.

A hybrid method for 3D mosaicing of OCT images of macula and Optic Nerve Head

A mosaiced image is the result of merging two or more images with overlapping area in order to generate a high resolution panorama of a large scene. A wide view of Optical Coherence Tomography (OCT) images can help clinicians in diagnosis by enabling simultaneous analysis of different portions of the gathered information. In this paper, we present a novel method for mosaicing of 3D OCT images of macula and Optic Nerve Head (ONH) that is carried out in two phases; registration of OCT projections and mosaicing of B-scans. In the first phase, in order to register the OCT projection images of macula and ONH, their corresponding color fundus image is considered as the main frame and the geometrical features of their curvelet-based extracted vessels are employed for registration. The registration parameters obtained are then applied on all x-y slices of the 3D OCT images of macula and ONH. In the B-scan mosaicing phase, the overlapping areas of corresponding reprojected B-scans are extracted and the best registration model is obtained based on line-by-line matching of corresponding A-scans in overlapping areas. This registration model is then applied to the remaining A-scans of the ONH-based B-scan. The aligned B-scans of macular OCT and OCT images of ONH are finally blended and 3D mosaiced OCT images are obtained. Two criteria are considered for assessment of mosaiced images; the quality of alignment/mosaicing of B-scans and the loss of clinical information from the B-scans after mosaicing. The average grading values of 3.5 ± 0.74 and 3.63 ± 0.55 (out of 4) are obtained for the first and second criteria, respectively.

Segmentation and Quantification for Angle-Closure Glaucoma Assessment in Anterior Segment OCT

Angle-closure glaucoma is a major cause of irreversible visual impairment and can be identified by measuring the anterior chamber angle (ACA) of the eye. The ACA can be viewed clearly through anterior segment optical coherence tomography (AS-OCT), but the imaging characteristics and the shapes and locations of major ocular structures can vary significantly among different AS-OCT modalities, thus complicating image analysis. To address this problem, we propose a data-driven approach for automatic AS-OCT structure segmentation, measurement, and screening. Our technique first estimates initial markers in the eye through label transfer from a hand-labeled exemplar data set, whose images are collected over different patients and AS-OCT modalities. These initial markers are then refined by using a graph-based smoothing method that is guided by AS-OCT structural information. These markers facilitate segmentation of major clinical structures, which are used to recover standard clinical parameters. These parameters can be used not only to support clinicians in making anatomical assessments, but also to serve as features for detecting anterior angle closure in automatic glaucoma screening algorithms. Experiments on Visante AS-OCT and Cirrus high-definition-OCT data sets demonstrate the effectiveness of our approach.

Avoiding Clinical Misinterpretation and Artifacts of Optical Coherence Tomography Analysis of the Optic Nerve, Retinal Nerve Fiber Layer, and Ganglion Cell Layer

Background:

Optical coherence tomography (OCT) has become an important tool for diagnosing optic nerve disease. The structural details and reproducibility of OCT continues to improve with further advances in technology. However, artifacts and misinterpretation of OCT can lead to clinical misdiagnosis of diseases if they go unrecognized.

Evidence Acquisition:

A literature review using PubMed combined with clinical and research experience.

Results:

We describe the most common artifacts and errors in interpretation seen on OCT in both optic nerve and ganglion cell analyses. We provide examples of the artifacts, discuss the causes, and provide methods of detecting them. In addition, we discuss a systematic approach to OCT analysis to facilitate the recognition of artifacts and to avoid clinical misinterpretation.

Conclusions:

While OCT is invaluable in diagnosing optic nerve disease, we need to be cognizant of the artifacts that can occur with OCT. Failure to recognize some of these artifacts can lead to misdiagnoses and inappropriate investigations.

Three-dimensional continuous max flow optimization-based serous retinal detachment segmentation in SD-OCT for central serous chorioretinopathy

Assessment of serous retinal detachment plays an important role in the diagnosis of central serous chorioretinopathy (CSC). In this paper, we propose an automatic, three-dimensional segmentation method to detect both neurosensory retinal detachment (NRD) and pigment epithelial detachment (PED) in spectral domain optical coherence tomography (SD-OCT) images. The proposed method involves constructing a probability map from training samples using random forest classification. The probability map is constructed from a linear combination of structural texture, intensity, and layer thickness information. Then, a continuous max flow optimization algorithm is applied to the probability map to segment the retinal detachment-associated fluid regions. Experimental results from 37 retinal SD-OCT volumes from cases of CSC demonstrate the proposed method can achieve a true positive volume fraction (TPVF), false positive volume fraction (FPVF), positive predicative value (PPV), and dice similarity coefficient (DSC) of 92.1%, 0.53%, 94.7%, and 93.3%, respectively, for NRD segmentation and 92.5%, 0.14%, 80.9%, and 84.6%, respectively, for PED segmentation. The proposed method can be an automatic tool to evaluate serous retinal detachment and has the potential to improve the clinical evaluation of CSC.

Optical Coherence Tomography Analysis Based Prediction of Humphrey 24-2 Visual Field Thresholds in Patients With Glaucoma

Purpose

A pilot study showed that prediction of individual Humphrey 24-2 visual field (HVF 24-2) sensitivity thresholds from optical coherence tomography (OCT) image analysis is possible. We evaluate performance of an improved approach as well as 3 other predictive algorithms on a new, fully independent set of glaucoma subjects.

Methods

Subjects underwent HVF 24-2 and 9-field OCT (Heidelberg Spectralis) testing. Nerve fiber (NFL), and ganglion cell and inner plexiform (GCL+IPL) layers were cosegmented and partitioned into 52 sectors matching HVF 24-2 test locations. The Wilcoxon rank sum test was applied to test correlation R, root mean square error (RMSE), and limits of agreement (LoA) between actual and predicted thresholds for four prediction models. The training data consisted of the 9-field OCT and HVF 24-2 thresholds of 111 glaucoma patients from our pilot study.

Results

We studied 112 subjects (112 eyes) with early, moderate, or advanced primary and secondary open angle glaucoma. Subjects with less than 9 scans (15/112) or insufficient quality segmentations (11/97) were excluded. Retinal ganglion cell axonal complex (RGC-AC) optimized had superior average R = 0.74 (95% confidence interval [CI], 0.67-0.76) and RMSE = 5.42 (95% CI, 5.1-5.7) dB, which was significantly better (P < 0.05/3) than the other three models: Naïve (R = 0.49; 95% CI, 0.44-0.54; RMSE = 7.24 dB; 95% CI, 6.6-7.8 dB), Garway-Heath (R = 0.66; 95% CI, 0.60-0.68; RMSE = 6.07 dB; 95% CI, 5.7-6.5 dB), and Donut (R = 0.67; 95% CI, 0.61-0.69; RMSE = 6.08 dB, 95% CI, 5.8-6.4 dB).

Conclusions

The proposed RGC-AC optimized predictive algorithm based on 9-field OCT image analysis and the RGC-AC concept is superior to previous methods and its performance is close to the reproducibility of HVF 24-2.

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.

Fully Automated Segmentation of Fluid/Cyst Regions in Optical Coherence Tomography Images With Diabetic Macular Edema Using Neutrosophic Sets and Graph Algorithms

This paper presents a fully automated algorithm to segment fluid-associated (fluid-filled) and cyst regions in optical coherence tomography (OCT) retina images of subjects with diabetic macular edema. The OCT image is segmented using a novel neutrosophic transformation and a graph-based shortest path method. In neutrosophic domain, an image is transformed into three sets: (true), (indeterminate) that represents noise, and (false). This paper makes four key contributions. First, a new method is introduced to compute the indeterminacy set , and a new -correction operation is introduced to compute the set in neutrosophic domain. Second, a graph shortest-path method is applied in neutrosophic domain to segment the inner limiting membrane and the retinal pigment epithelium as regions of interest (ROI) and outer plexiform layer and inner segment myeloid as middle layers using a novel definition of the edge weights . Third, a new cost function for cluster-based fluid/cyst segmentation in ROI is presented which also includes a novel approach in estimating the number of clusters in an automated manner. Fourth, the final fluid regions are achieved by ignoring very small regions and the regions between middle layers. The proposed method is evaluated using two publicly available datasets: Duke, Optima, and a third local dataset from the UMN clinic which is available online. The proposed algorithm outperforms the previously proposed Duke algorithm by 8% with respect to the dice coefficient and by 5% with respect to precision on the Duke dataset, while achieving about the same sensitivity. Also, the proposed algorithm outperforms a prior method for Optima dataset by 6%, 22%, and 23% with respect to the dice coefficient, sensitivity, and precision, respectively. Finally, the proposed algorithm also achieves sensitivity of 67.3%, 88.8%, and 76.7%, for the Duke, Optima, and the university of minnesota (UMN) datasets, respectively.

Peripapillary Retinal Pigment Epithelium Layer Shape Changes From Acetazolamide Treatment in the Idiopathic Intracranial Hypertension Treatment Trial

Purpose

Recent studies indicate that the amount of deformation of the peripapillary retinal pigment epithelium and Bruch's membrane (pRPE/BM) toward or away from the vitreous may reflect acute changes in cerebrospinal fluid pressure. The study purpose is to determine if changes in optic-nerve-head (ONH) shape reflect a treatment effect (acetazolamide/placebo + weight management) using the optical coherence tomography (OCT) substudy of the Idiopathic Intracranial Hypertension Treatment Trial (IIHTT) at baseline, 3, and 6 months.

Methods

The pRPE/BM shape deformation was quantified and compared with ONH volume, peripapillary retinal nerve fiber layer (pRNFL), and total retinal (pTR) thicknesses in the acetazolamide group (39 subjects) and placebo group (31 subjects) at baseline, 3, and 6 months.

Results

Mean changes of the pRPE/BM shape measure were significant and in the positive direction (away from the vitreous) for the acetazolamide group (P < 0.01), but not for the placebo group. The three OCT measures reflecting the reduction of optic disc swelling were significant in both treatment groups but greater in the acetazolamide group (P < 0.01).

Conclusions

Change in the pRPE/BM shape away from the vitreous reflects the effect of acetazolamide + weight management in reducing the pressure differential between the intraocular and retrobulbar arachnoid space. Weight management alone was also associated with a decrease in optic nerve volume/edema but without a significant change in the pRPE/BM shape, implying an alternative mechanism for improvement in papilledema and axoplasmic flow, independent of a reduction in the pressure differential. (ClinicalTrials.gov number, NCT01003639.)

Automated framework for intraretinal cystoid macular edema segmentation in three-dimensional optical coherence tomography images with macular hole

Cystoid macular edema (CME) and macular hole (MH) are the leading causes for visual loss in retinal diseases. The volume of the CMEs can be an accurate predictor for visual prognosis. This paper presents an automatic method to segment the CMEs from the abnormal retina with coexistence of MH in three-dimensional-optical coherence tomography images. The proposed framework consists of preprocessing and CMEs segmentation. The preprocessing part includes denoising, intraretinal layers segmentation and flattening, and MH and vessel silhouettes exclusion. In the CMEs segmentation, a three-step strategy is applied. First, an AdaBoost classifier trained with 57 features is employed to generate the initialization results. Second, an automated shape-constrained graph cut algorithm is applied to obtain the refined results. Finally, cyst area information is used to remove false positives (FPs). The method was evaluated on 19 eyes with coexistence of CMEs and MH from 18 subjects. The true positive volume fraction, FP volume fraction, dice similarity coefficient, and accuracy rate for CMEs segmentation were 81.0%±7.8%, 0.80%±0.63%, 80.9%±5.7%, and 99.7%±0.1%, respectively.

The Pattern of Visual Fixation Eccentricity and Instability in Optic Neuropathy and Its Spatial Relationship to Retinal Ganglion Cell Layer Thickness

Purpose

The purpose of this study was to assess whether clinically useful measures of fixation instability and eccentricity can be derived from retinal tracking data obtained during optical coherence tomography (OCT) in patients with optic neuropathy (ON) and to develop a method for relating fixation to the retinal ganglion cell complex (GCC) thickness.

Methods

Twenty-nine patients with ON underwent macular volume OCT with 30 seconds of confocal scanning laser ophthalmoscope (cSLO)-based eye tracking during fixation. Kernel density estimation quantified fixation instability and fixation eccentricity from the distribution of fixation points on the retina. Preferred ganglion cell layer loci (PGCL) and their relationship to the GCC thickness map were derived, accounting for radial displacement of retinal ganglion cell soma from their corresponding cones.

Results

Fixation instability was increased in ON eyes (0.21 deg²) compared with normal eyes (0.06982 deg²; P < 0.001), and fixation eccentricity was increased in ON eyes (0.48°) compared with normal eyes (0.24°; P = 0.03). Fixation instability and eccentricity each correlated moderately with logMAR acuity and were highly predictive of central visual field loss. Twenty-six of 35 ON eyes had PGCL skewed toward local maxima of the GCC thickness map. Patients with bilateral dense central scotomas had PGCL in homonymous retinal locations with respect to the fovea.

Conclusions

Fixation instability and eccentricity measures obtained during cSLO-OCT assess the function of perifoveal retinal elements and predict central visual field loss in patients with ON. A model relating fixation to the GCC thickness map offers a method to assess the structure–function relationship between fixation and areas of preserved GCC in patients with ON.

A machine-learning graph-based approach for 3D segmentation of Bruch's membrane opening from glaucomatous SD-OCT volumes

Bruch's membrane opening-minimum rim width (BMO-MRW) is a recently proposed structural parameter which estimates the remaining nerve fiber bundles in the retina and is superior to other conventional structural parameters for diagnosing glaucoma. Measuring this structural parameter requires identification of BMO locations within spectral domain-optical coherence tomography (SD-OCT) volumes. While most automated approaches for segmentation of the BMO either segment the 2D projection of BMO points or identify BMO points in individual B-scans, in this work, we propose a machine-learning graph-based approach for true 3D segmentation of BMO from glaucomatous SD-OCT volumes. The problem is formulated as an optimization problem for finding a 3D path within the SD-OCT volume. In particular, the SD-OCT volumes are transferred to the radial domain where the closed loop BMO points in the original volume form a path within the radial volume. The estimated location of BMO points in 3D are identified by finding the projected location of BMO points using a graph-theoretic approach and mapping the projected locations onto the Bruch's membrane (BM) surface. Dynamic programming is employed in order to find the 3D BMO locations as the minimum-cost path within the volume. In order to compute the cost function needed for finding the minimum-cost path, a random forest classifier is utilized to learn a BMO model, obtained by extracting intensity features from the volumes in the training set, and computing the required 3D cost function. The proposed method is tested on 44 glaucoma patients and evaluated using manual delineations. Results show that the proposed method successfully identifies the 3D BMO locations and has significantly smaller errors compared to the existing 3D BMO identification approaches.

Automatic Subretinal Fluid Segmentation of Retinal SD-OCT Images With Neurosensory Retinal Detachment Guided by Enface Fundus Imaging

OBJECTIVE

Accurate segmentation of neurosensory retinal detachment (NRD) associated subretinal fluid in spectral domain optical coherence tomography (SD-OCT) is vital for the assessment of central serous chorioretinopathy (CSC). A novel two-stage segmentation algorithm was proposed, guided by Enface fundus imaging.

METHODS

In the first stage, Enface fundus image was segmented using thickness map prior to detecting the fluid-associated abnormalities with diffuse boundaries. In the second stage, the locations of the abnormalities were used to restrict the spatial extent of the fluid region, and a fuzzy level set method with a spatial smoothness constraint was applied to subretinal fluid segmentation in the SD-OCT scans.

RESULTS

Experimental results from 31 retinal SD-OCT volumes with CSC demonstrate that our method can achieve a true positive volume fraction (TPVF), false positive volume fraction (FPVF), and positive predicative value (PPV) of 94.3%, 0.97%, and 93.6%, respectively, for NRD regions. Our approach can also discriminate NRD-associated subretinal fluid from subretinal pigment epithelium fluid associated with pigment epithelial detachment with a TPVF, FPVF, and PPV of 93.8%, 0.40%, and 90.5%, respectively.

CONCLUSION

We report a fully automatic method for the segmentation of subretinal fluid.

SIGNIFICANCE

Our method shows the potential to improve clinical therapy for CSC.

Automated boundary detection of the optic disc and layer segmentation of the peripapillary retina in volumetric structural and angiographic optical coherence tomography

To improve optic disc boundary detection and peripapillary retinal layer segmentation, we propose an automated approach for structural and angiographic optical coherence tomography. The algorithm was performed on radial cross-sectional B-scans. The disc boundary was detected by searching for the position of Bruch's membrane opening, and retinal layer boundaries were detected using a dynamic programming-based graph search algorithm on each B-scan without the disc region. A comparison of the disc boundary using our method with that determined by manual delineation showed good accuracy, with an average Dice similarity coefficient ≥0.90 in healthy eyes and eyes with diabetic retinopathy and glaucoma. The layer segmentation accuracy in the same cases was on average less than one pixel (3.13 μm).

Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images

Non-lethal macular diseases greatly impact patients' life quality, and will cause vision loss at the late stages. Visual inspection of the optical coherence tomography (OCT) images by the experienced clinicians is the main diagnosis technique. We proposed a computer-aided diagnosis (CAD) model to discriminate age-related macular degeneration (AMD), diabetic macular edema (DME) and healthy macula. The linear configuration pattern (LCP) based features of the OCT images were screened by the Correlation-based Feature Subset (CFS) selection algorithm. And the best model based on the sequential minimal optimization (SMO) algorithm achieved 99.3% in the overall accuracy for the three classes of samples.

Multimodal registration of SD-OCT volumes and fundus photographs using histograms of oriented gradients

With availability of different retinal imaging modalities such as fundus photography and spectral domain optical coherence tomography (SD-OCT), having a robust and accurate registration scheme to enable utilization of this complementary information is beneficial. The few existing fundus-OCT registration approaches contain a vessel segmentation step, as the retinal blood vessels are the most dominant structures that are in common between the pair of images. However, errors in the vessel segmentation from either modality may cause corresponding errors in the registration. In this paper, we propose a feature-based registration method for registering fundus photographs and SD-OCT projection images that benefits from vasculature structural information without requiring blood vessel segmentation. In particular, after a preprocessing step, a set of control points (CPs) are identified by looking for the corners in the images. Next, each CP is represented by a feature vector which encodes the local structural information via computing the histograms of oriented gradients (HOG) from the neighborhood of each CP. The best matching CPs are identified by calculating the distance of their corresponding feature vectors. After removing the incorrect matches the best affine transform that registers fundus photographs to SD-OCT projection images is computed using the random sample consensus (RANSAC) method. The proposed method was tested on 44 pairs of fundus and SD-OCT projection images of glaucoma patients and the result showed that the proposed method successfully registers the multimodal images and produced a registration error of 25.34 ± 12.34 μm (0.84 ± 0.41 pixels).

Diagnostic Performance of a Novel Three-Dimensional Neuroretinal Rim Parameter for Glaucoma Using High-Density Volume Scans

PURPOSE

To evaluate the diagnostic performance of a 3-dimensional (3D) neuroretinal rim parameter, the minimum distance band (MDB), using optical coherence tomography (OCT) high-density volume scans for open-angle glaucoma.

DESIGN

Reliability analysis.

METHODS

setting: Institutional.

STUDY POPULATION

Total of 163 patients (105 glaucoma and 58 healthy subjects).

OBSERVATION PROCEDURES

One eye of each patient was included. MDB and retinal nerve fiber layer (RNFL) thickness values were determined for 4 quadrants and 4 sectors using a spectral-domain OCT device.

MAIN OUTCOME MEASURES

Area under the receiver operating characteristic curve (AUROC) values, sensitivities, specificities, and positive and negative predictive values.

RESULTS

The best AUROC values of 3D MDB thickness for glaucoma and early glaucoma were for the overall globe (0.969, 0.952), followed by the inferior quadrant (0.966, 0.949) and inferior-temporal sector (0.966, 0.944), and then followed by the superior-temporal sector (0.964, 0.932) and superior quadrant (0.962, 0.924). All 3D MDB thickness AUROC values were higher than those of 2D RNFL thickness. Pairwise comparisons showed that the diagnostic performance of the 3D MDB parameter was significantly better than 2D RNFL thickness only for the nasal quadrant and inferior-nasal and superior-nasal sectors (P = .023-.049). Combining 3D MDB with 2D RNFL parameters provided significantly better diagnostic performance (AUROC 0.984) than most single MDB parameters and all single RNFL parameters.

CONCLUSIONS

Compared with the 2D RNFL thickness parameter, the 3D MDB neuroretinal rim thickness parameter had uniformly equal or better diagnostic performance for glaucoma in all regions and was significantly better in the nasal region.

Incorporation of gradient vector flow field in a multimodal graph-theoretic approach for segmenting the internal limiting membrane from glaucomatous optic nerve head-centered SD-OCT volumes

The internal limiting membrane (ILM) separates the retina and optic nerve head (ONH) from the vitreous. In the optical coherence tomography volumes of glaucoma patients, while current approaches for the segmentation of the ILM in the peripapillary and macular regions are considered robust, current approaches commonly produce ILM segmentation errors at the ONH due to the presence of blood vessels and/or characteristic glaucomatous deep cupping. Because a precise segmentation of the ILM surface at the ONH is required for computing several newer structural measurements including Bruch's membrane opening-minimum rim width (BMO-MRW) and cup volume, in this study, we propose a multimodal multiresolution graph-based method to precisely segment the ILM surface within ONH-centered spectral-domain optical coherence tomography (SD-OCT) volumes. In particular, the gradient vector flow (GVF) field, which is computed from a multiresolution initial segmentation, is employed for calculating a set of non-overlapping GVF-based columns perpendicular to the initial segmentation. The GVF columns are utilized to resample the volume and also serve as the columns to the graph construction. The ILM surface in the resampled volume is fairly smooth and does not contain the steep slopes. This prior shape knowledge along with the blood vessel information, obtained from registered fundus photographs, are incorporated in a graph-theoretic approach in order to identify the location of the ILM surface. The proposed method is tested on the SD-OCT volumes of 44 subjects with various stages of glaucoma and significantly smaller segmentation errors were obtained than that of current approaches.

Automatic Three-dimensional Detection of Photoreceptor Ellipsoid Zone Disruption Caused by Trauma in the OCT

Detection and assessment of the integrity of the photoreceptor ellipsoid zone (EZ) are important because it is critical for visual acuity in retina trauma and other diseases. We have proposed and validated a framework that can automatically analyse the 3D integrity of the EZ in optical coherence tomography (OCT) images. The images are first filtered and automatically segmented into 10 layers, of which EZ is located in the 7^th layer. For each voxel of the EZ, 57 features are extracted and a principle component analysis is performed to optimize the features. An Adaboost classifier is trained to classify each voxel of the EZ as disrupted or non-disrupted. Finally, blood vessel silhouettes and isolated points are excluded. To demonstrate its effectiveness, the proposed framework was tested on 15 eyes with retinal trauma and 15 normal eyes. For the eyes with retinal trauma, the sensitivity (SEN) was 85.69% ± 9.59%, the specificity (SPE) was 85.91% ± 5.48%, and the balanced accuracy rate (BAR) was 85.80% ± 6.16%. For the normal eyes, the SPE was 99.03% ± 0.73%, and the SEN and BAR levels were not relevant. Our framework has the potential to become a useful tool for studying retina trauma and other conditions involving EZ integrity.

Simultaneous Segmentation of Retinal Surfaces and Microcystic Macular Edema in SDOCT Volumes

Optical coherence tomography (OCT) is a noninvasive imaging modality that has begun to find widespread use in retinal imaging for the detection of a variety of ocular diseases. In addition to structural changes in the form of altered retinal layer thicknesses, pathological conditions may also cause the formation of edema within the retina. In multiple sclerosis, for instance, the nerve fiber and ganglion cell layers are known to thin. Additionally, the formation of pseudocysts called microcystic macular edema (MME) have also been observed in the eyes of about 5% of MS patients, and its presence has been shown to be correlated with disease severity. Previously, we proposed separate algorithms for the segmentation of retinal layers and MME, but since MME mainly occurs within specific regions of the retina, a simultaneous approach is advantageous. In this work, we propose an automated globally optimal graph-theoretic approach that simultaneously segments the retinal layers and the MME in volumetric OCT scans. SD-OCT scans from one eye of 12 MS patients with known MME and 8 healthy controls were acquired and the pseudocysts manually traced. The overall precision and recall of the pseudocyst detection was found to be 86.0% and 79.5%, respectively.

Automated Segmentability Index for Layer Segmentation of Macular SD-OCT Images

PURPOSE

To automatically identify which spectral-domain optical coherence tomography (SD-OCT) scans will provide reliable automated layer segmentations for more accurate layer thickness analyses in population studies.

METHODS

Six hundred ninety macular SD-OCT image volumes (6.0 × 6.0 × 2.3 mm3) were obtained from one eyes of 690 subjects (74.6 ± 9.7 [mean ± SD] years, 37.8% of males) randomly selected from the population-based Rotterdam Study. The dataset consisted of 420 OCT volumes with successful automated retinal nerve fiber layer (RNFL) segmentations obtained from our previously reported graph-based segmentation method and 270 volumes with failed segmentations. To evaluate the reliability of the layer segmentations, we have developed a new metric, segmentability index SI, which is obtained from a random forest regressor based on 12 features using OCT voxel intensities, edge-based costs, and on-surface costs. The SI was compared with well-known quality indices, quality index (QI), and maximum tissue contrast index (mTCI), using receiver operating characteristic (ROC) analysis.

RESULTS

The 95% confidence interval (CI) and the area under the curve (AUC) for the QI are 0.621 to 0.805 with AUC 0.713, for the mTCI 0.673 to 0.838 with AUC 0.756, and for the SI 0.784 to 0.920 with AUC 0.852. The SI AUC is significantly larger than either the QI or mTCI AUC (P < 0.01).

CONCLUSIONS

The segmentability index SI is well suited to identify SD-OCT scans for which successful automated intraretinal layer segmentations can be expected.

TRANSLATIONAL RELEVANCE

Interpreting the quantification of SD-OCT images requires the underlying segmentation to be reliable, but standard SD-OCT quality metrics do not predict which segmentations are reliable and which are not. The segmentability index SI presented in this study does allow reliable segmentations to be identified, which is important for more accurate layer thickness analyses in research and population studies.

An automated framework for 3D serous pigment epithelium detachment segmentation in SD-OCT images

Pigment epithelium detachment (PED) is an important clinical manifestation of multiple chorioretinal diseases, which can cause loss of central vision. In this paper, an automated framework is proposed to segment serous PED in SD-OCT images. The proposed framework consists of four main steps: first, a multi-scale graph search method is applied to segment abnormal retinal layers; second, an effective AdaBoost method is applied to refine the initial segmented regions based on 62 extracted features; third, a shape-constrained graph cut method is applied to segment serous PED, in which the foreground and background seeds are obtained automatically; finally, an adaptive structure elements based morphology method is applied to remove false positive segmented regions. The proposed framework was tested on 25 SD-OCT volumes from 25 patients diagnosed with serous PED. The average true positive volume fraction (TPVF), false positive volume fraction (FPVF), dice similarity coefficient (DSC) and positive predictive value (PPV) are 90.08%, 0.22%, 91.20% and 92.62%, respectively. The proposed framework can provide clinicians with accurate quantitative information, including shape, size and position of the PED region, which can assist clinical diagnosis and treatment.

Automated segmentation of optic disc in SD-OCT images and cup-to-disc ratios quantification by patch searching-based neural canal opening detection

Glaucoma is one of the most common causes of blindness worldwide. Early detection of glaucoma is traditionally based on assessment of the cup-to-disc (C/D) ratio, an important indicator of structural changes to the optic nerve head. Here, we present an automated optic disc segmentation algorithm in 3-D spectral domain optical coherence tomography (SD-OCT) volumes to quantify this ratio. The proposed algorithm utilizes a two-stage strategy. First, it detects the neural canal opening (NCO) by finding the points with maximum curvature on the retinal pigment epithelium (RPE) boundary with a spatial correlation smoothness constraint on consecutive B-scans, and it approximately locates the coarse disc margin in the projection image using convex hull fitting. Then, a patch searching procedure using a probabilistic support vector machine (SVM) classifier finds the most likely patch with the NCO in its center in order to refine the segmentation result. Thus, a reference plane can be determined to calculate the C/D radio. Experimental results on 42 SD-OCT volumes from 17 glaucoma patients demonstrate that the proposed algorithm can achieve high segmentation accuracy and a low C/D ratio evaluation error. The unsigned border error for optic disc segmentation and the evaluation error for C/D ratio comparing with manual segmentation are 2.216 ± 1.406 pixels (0.067 ± 0.042 mm) and 0.045 ± 0.033, respectively.

A hierarchical framework for estimating neuroretinal rim area using 3D spectral domain optical coherence tomography (SD-OCT) optic nerve head (ONH) images of healthy and glaucoma eyes

Glaucoma is a chronic neurodegenerative disease characterized by loss of retinal ganglion cells, resulting in distinctive changes in the optic nerve head (ONH) and retinal nerve fiber layer (RNFL). Important advances in technology for non-invasive imaging of the eye have been made providing quantitative tools to measure structural changes in ONH topography, a crucial step in diagnosing and monitoring glaucoma. 3D spectral domain optical coherence tomography (SD-OCT), an optical imaging technique, has been commonly used to discriminate glaucomatous from healthy subjects. In this paper, we present a new approach for locating the Bruch's membrane opening BMO and then estimating the optic disc size and rim area of 3D Spectralis SD-OCT images. To deal with the overlapping of the Bruch's membrane BM layer and the border tissue of Elschnig due to the poor image resolution, we propose the use of image deconvolution approach to separate these layers. To estimate the optic disc size and rim area, we propose the use of a new regression method based on the artificial neural network principal component analysis (ANN-PCA), which allows us to model irregularity in the BMO estimation due to scan shifts and/or poor image quality. The diagnostic accuracy of rim area, and rim to disc area ratio is compared to the diagnostic accuracy of global RNFL thickness measurements provided by two commercially available SD-OCT devices using receiver operating characteristic curve analyses.

Automatic segmentation of the optic nerve head for deformation measurements in video rate optical coherence tomography

Optical coherence tomography (OCT) imaging has become a standard diagnostic tool in ophthalmology, providing essential information associated with various eye diseases. In order to investigate the dynamics of the ocular fundus, we present a simple and accurate automated algorithm to segment the inner limiting membrane in video-rate optic nerve head spectral domain (SD) OCT images. The method is based on morphological operations including a two-step contrast enhancement technique, proving to be very robust when dealing with low signal-to-noise ratio images and pathological eyes. An analysis algorithm was also developed to measure neuroretinal tissue deformation from the segmented retinal profiles. The performance of the algorithm is demonstrated, and deformation results are presented for healthy and glaucomatous eyes.

Causes and Prognosis of Visual Acuity Loss at the Time of Initial Presentation in Idiopathic Intracranial Hypertension

PURPOSE

To determine the etiology and prognosis of visual acuity loss in idiopathic intracranial hypertension (IIH) at presentation and to provide objective measures to predict visual outcome.

METHODS

A retrospective review of 660 patients with IIH (2009-2013) identified 31 patients (4.7%) with 48 eyes having best-corrected visual acuity (BCVA) of 20/25 or worse on initial presentation. Fundus photography, optical coherence tomography (OCT) of the optic disc and macula, and perimetry were used to determine the causes and prognosis of vision loss. Segmentation of the macula OCT was performed using the Iowa Reference Algorithm to determine the retinal ganglion cell-inner plexiform layer complex (GCL-IPL) thickness.

RESULTS

Outer retinal changes alone caused decreased BCVA at initial presentation in 22 eyes (46%): subretinal fluid in 16, chorioretinal folds in 5, and peripapillary choroidal neovascularization in 1. The vision loss was reversible except for some eyes with chorioretinal folds. Optic neuropathy alone caused decreased BCVA in 10 eyes (21%) and coexisting outer retinal changes and optic neuropathy caused decreased BCVA in 16 eyes (33%). A GCL-IPL thickness less than or equal to 70 μm at initial presentation or progressive thinning of greater than or equal to 10 μm within 2 to 3 weeks compared with baseline correlated with poor visual outcome.

CONCLUSIONS

Visual acuity loss in IIH can be caused by both outer retinal changes and optic neuropathy. Vision loss from outer retinal changes is mostly reversible. The outcome of patients with coexisting outer retinal changes and optic neuropathy or optic neuropathy alone depends on the degree of optic neuropathy, which can be predicted by the GCL-IPL thickness.

Multimodal Segmentation of Optic Disc and Cup From SD-OCT and Color Fundus Photographs Using a Machine-Learning Graph-Based Approach

In this work, a multimodal approach is proposed to use the complementary information from fundus photographs and spectral domain optical coherence tomography (SD-OCT) volumes in order to segment the optic disc and cup boundaries. The problem is formulated as an optimization problem where the optimal solution is obtained using a machine-learning theoretical graph-based method. In particular, first the fundus photograph is registered to the 2D projection of the SD-OCT volume. Three in-region cost functions are designed using a random forest classifier corresponding to three regions of cup, rim, and background. Next, the volumes are resampled to create radial scans in which the Bruch's Membrane Opening (BMO) endpoints are easier to detect. Similar to in-region cost function design, the disc-boundary cost function is designed using a random forest classifier for which the features are created by applying the Haar Stationary Wavelet Transform (SWT) to the radial projection image. A multisurface graph-based approach utilizes the in-region and disc-boundary cost images to segment the boundaries of optic disc and cup under feasibility constraints. The approach is evaluated on 25 multimodal image pairs from 25 subjects in a leave-one-out fashion (by subject). The performances of the graph-theoretic approach using three sets of cost functions are compared: 1) using unimodal (OCT only) in-region costs, 2) using multimodal in-region costs, and 3) using multimodal in-region and disc-boundary costs. Results show that the multimodal approaches outperform the unimodal approach in segmenting the optic disc and cup.

Characterizing the Impact of Off-Axis Scan Acquisition on the Reproducibility of Total Retinal Thickness Measurements in SDOCT Volumes

PURPOSE

Off-axis acquisition of spectral domain optical coherence tomography (SDOCT) images has been shown to increase total retinal thickness (TRT) measurements. We analyzed the reproducibility of TRT measurements obtained using either the retinal pigment epithelium (RPE) or Bruch's membrane as reference surfaces in off-axis scans intentionally acquired through multiple pupil positions.

METHODS

Five volumetric SDOCT scans of the macula were obtained from one eye of 25 normal subjects. One scan was acquired through a central pupil position, while subsequent scans were acquired through four peripheral pupil positions. The internal limiting membrane, the RPE, and Bruch's membrane were segmented using automated approaches. These volumes were registered to each other and the TRT was evaluated in 9 Early Treatment of Diabetic Retinopathy Study (ETDRS) regions. The reproducibility of the TRT obtained using the RPE was computed using the mean difference, coefficient of variation (CV), and the intraclass correlation coefficient (ICC), and compared to those obtained using Bruch's membrane as the reference surface. A secondary set of 1545 SDOCT scans was also analyzed in order to gauge the incidence of off-axis scans in a typical acquisition environment.

RESULTS

The photoreceptor tips were dimmer in off-axis images, which affected the RPE segmentation. The overall mean TRT difference and CV obtained using the RPE were 7.04 ± 4.31 μm and 1.46%, respectively, whereas Bruch's membrane was 1.16 ± 1.00 μm and 0.32%, respectively. The ICCs at the subfoveal TRT were 0.982 and 0.999, respectively. Forty-one percent of the scans in the secondary set showed large tilts (> 6%).

CONCLUSIONS

RPE segmentation is confounded by its proximity to the interdigitation zone, a structure strongly affected by the optical Stiles-Crawford effect. Bruch's membrane, however, is unaffected leading to a more robust segmentation that is less dependent upon pupil position.

TRANSLATIONAL RELEVANCE

The way in which OCT images are acquired can independently affect the accuracy of automated retinal thickness measurements. Assessment of scan angle in a clinical dataset demonstrates that off-axis scans are common, which emphasizes the need for caution when relying on automated thickness parameters when this component of scan acquisition is not controlled for.

Thickness mapping of eleven retinal layers segmented using the diffusion maps method in normal eyes

This study was conducted to determine the thickness map of eleven retinal layers in normal subjects by spectral domain optical coherence tomography (SD-OCT) and evaluate their association with sex and age. Mean regional retinal thickness of 11 retinal layers was obtained by automatic three-dimensional diffusion map based method in 112 normal eyes of 76 Iranian subjects. We applied our previously reported 3D intraretinal fast layer segmentation which does not require edge-based image information but rather relies on regional image texture. The thickness maps are compared among 9 macular sectors within 3 concentric circles as defined by ETDRS. The thickness map of central foveal area in layers 1, 3, and 4 displayed the minimum thickness. Maximum thickness was observed in nasal to the fovea of layer 1 and in a circular pattern in the parafoveal retinal area of layers 2, 3, and 4 and in central foveal area of layer 6. Temporal and inferior quadrants of the total retinal thickness and most of other quadrants of layer 1 were significantly greater in the men than in the women. Surrounding eight sectors of total retinal thickness and a limited number of sectors in layers 1 and 4 significantly correlated with age.

Automated 3-D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments

Automated retinal layer segmentation of optical coherence tomography (OCT) images has been successful for normal eyes but becomes challenging for eyes with retinal diseases if the retinal morphology experiences critical changes. We propose a method to automatically segment the retinal layers in 3-D OCT data with serous retinal pigment epithelial detachments (PED), which is a prominent feature of many chorioretinal disease processes. The proposed framework consists of the following steps: fast denoising and B-scan alignment, multi-resolution graph search based surface detection, PED region detection and surface correction above the PED region. The proposed technique was evaluated on a dataset with OCT images from 20 subjects diagnosed with PED. The experimental results showed the following. 1) The overall mean unsigned border positioning error for layer segmentation is 7.87±3.36 μm , and is comparable to the mean inter-observer variability ( 7.81±2.56 μm). 2) The true positive volume fraction (TPVF), false positive volume fraction (FPVF) and positive predicative value (PPV) for PED volume segmentation are 87.1%, 0.37%, and 81.2%, respectively. 3) The average running time is 220 s for OCT data of 512 × 64 × 480 voxels.