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OCT-A is a novel imaging technique that extracts volumetric images of microvascular circulation from the data of OCT images.

Optical coherence tomography (OCT) is an imaging technic that was first applied for eye length measurements in 1988 and then in 1991 for creating in vivo retinal images. During ensuing years, it became an essential part of ocular examination in most eye clinics. It uses low coherence interferometry technique to create two or three-dimensional in vivo images of ocular structures. Optical coherence tomography utilizes near infrared laser waves that can penetrate ocular media with minimal scattering to achieve high quality images. The axial resolution is high enough (2–3 microm) to make OCT the imaging method of choice for evaluating retinal microanatomy in many conditions. Using near infrared light and being a noninvasive technique, OCT is generally considered a harmless imaging method.

Age-related macular degeneration (ARMD) is the main cause of irreversible visual loss affecting 10–15% of adults over 65 years of age in developed countries. It has traditionally been categorized into two major types: non-exudative or “dry” AMD (drusen, non-neovascular pigment epithelial detachments (PEDs), macular pigmentary alterations and migrations and geographic or non-geographic atrophy) and exudative or “wet” AMD (neovascularization of any type). In this chapter we discussed the multimodal imaging of these types and the clues for differentiation between them and their roles in treatment policies

One of the most common degenerative retinal disorders is “pathologic myopia that is defined by an axial length of more than 26.5 mm (high myopia) in the presence of posterior staphyloma.

Indocyanine green (ICG) is a high-molecular-weight (775 Da), water-soluble dye with dark green color and has maximum optical absorption at 800 nm and maximum emission at 835 nm in the near-infrared wavelength.

Vitreomacular interface is the plane where the posterior vitreous cortex attaches the inner limiting membrane (ILM).

Background: Macular disorders, such as diabetic macular edema (DME) and age-related macular degeneration (AMD) are among the major ocular diseases. Having one of these diseases can lead to vision impairments or even permanent blindness in a not-so-long time span. So, the early diagnosis of these diseases are the main goals of eye researchers. Material and Methods: The present study is designed in order to present a comparative study on the recent convolutional mixture of experts (CMoE) models for distinguishing normal macular OCT from DME and AMD. For this purpose, we considered three recent CMoE models called Mixture ensemble of convolutional neural networks (MECNN), Multi-scale Convolutional Mixture of Experts (MCME), and Wavelet-based Convolutional Mixture of Experts (WCME) models. For this research study, the models were evaluated on a database of three different macular OCT sets. Two first OCT sets were acquired by Heidelberg imaging systems consisting of 148 and 45 subjects respectively and set3 was constituted of 384 Bioptigen OCT acquisitions. To provide better performance insight into the CMoE ensembles, we extensively analyzed the models based on the 5-fold cross-validation method and various classification measures such as precision and average area under the ROC curve (AUC). Results: Experimental evaluations showed that the MCME and WCME outperformed the ME-CNN model and presented overall precisions of 98.14% and 96.06% for aligned OCTs respectively. For non-aligned retinal OCTs, these values were 93.95% and 95.56%. Conclusion: Based on the comparative analysis, although the MCME model outperformed the other CMoE models in the analysis of aligned retinal OCTs, the WCME offers a robust model for diagnosis of non-aligned retinal OCTs. This allows having a fast and robust computer aided system in macular OCT imaging which does not rely on the routine computerized processes such as denoising, segmentation of retinal layers, and also retinal layers alignment.

Optical coherence tomography (OCT) is a recently emerging non-invasive diagnostic tool useful in several medical applications such as ophthalmology, cardiology, gastroenterology and dermatology. One of the major problems with OCT pertains to its low contrast due to the presence of multiplicative speckle noise, which limits the signal-to-noise ratio (SNR) and obscures low-intensity and small features. In this paper, we recommend a new method using the 3D curvelet based K-times singular value decomposition (K-SVD) algorithm for speckle noise reduction and contrast enhancement of the intra-retinal layers of 3D Spectral-Domain OCT (3D-SDOCT) images. In order to benefit from the near-optimum properties of curvelet transform (such as good directional selectivity) on top of dictionary learning, we propose a new plan in dictionary learning by using the curvelet atoms as the initial dictionary. For this reason, the curvelet transform of the noisy image is taken and then the noisy coefficients matrix in each scale, rotation and spatial coordinates is passed through the K-SVD denoising algorithm with predefined 3D initial dictionary that is adaptively selected from thresholded coefficients in the same subband of the image. During the denoising of curvelet coefficients, we can also modify them for the purpose of contrast enhancement of intra-retinal layers. We demonstrate the ability of our proposed algorithm in the speckle noise reduction of 17 publicly available 3D OCT data sets, each of which contains 100 B-scans of size 512×1000 with and without neovascular age-related macular degeneration (AMD) images acquired using SDOCT, Bioptigen imaging systems. Experimental results show that an improvement from 1.27 to 7.81 in contrast to noise ratio (CNR), and from 38.09 to 1983.07 in equivalent number of looks (ENL) is achieved, which would outperform existing state-of-the-art OCT despeckling methods.

Computer-aided diagnosis (CAD) of retinal pathologies is a current active area in medical image analysis. Due to the increasing use of retinal optical coherence tomography (OCT) imaging technique, a CAD system in retinal OCT is essential to assist ophthalmologist in the early detection of ocular diseases and treatment monitoring. This paper presents a novel CAD system based on a multi-scale convolutional mixture of expert (MCME) ensemble model to identify normal retina, and two common types of macular pathologies, namely, dry age-related macular degeneration, and diabetic macular edema. The proposed MCME modular model is a data-driven neural structure, which employs a new cost function for discriminative and fast learning of image features by applying convolutional neural networks on multiple-scale sub-images. MCME maximizes the likelihood function of the training data set and ground truth by considering a mixture model, which tries also to model the joint interaction between individual experts by using a correlated multivariate component for each expert module instead of only modeling the marginal distributions by independent Gaussian components. Two different macular OCT data sets from Heidelberg devices were considered for the evaluation of the method, i.e., a local data set of OCT images of 148 subjects and a public data set of 45 OCT acquisitions. For comparison purpose, we performed a wide range of classification measures to compare the results with the best configurations of the MCME method. With the MCME model of four scale-dependent experts, the precision rate of 98.86%, and the area under the receiver operating characteristic curve (AUC) of 0.9985 were obtained on average.

This paper presents a new three-dimensional curvelet transform based dictionary learning for automatic segmentation of intraretinal cysts, most relevant prognostic biomarker in neovascular age-related macular degeneration, from 3D spectral-domain optical coherence tomography. (SD-OCT) images. In particular, we focus on the Spectralis SD-OCT. (Heidelberg Engineering, Heidelberg, Germany) system, and show the applicability of our algorithm in the segmentation of these features. For this purpose, we use recursive Gaussian filter and approximate the corrupted pixels from its surrounding, then in order to enhance the cystoid dark space regions and future noise suppression we introduce a new scheme in dictionary learning and take curvelet transform of filtered image then denoise and modify each noisy coefficients matrix in each scale with predefined initial 3D sparse dictionary. Dark pixels between retinal pigment epithelium and nerve fiber layer that were extracted with graph theory are considered as cystoid spaces. The average dice coefficient for the segmentation of cystoid regions in whole 3D volume and with-in central 3. mm diameter on the MICCAI 2015 OPTIMA Cyst Segmentation Challenge dataset were found to be 0.65 and 0.77, respectively.

Anterior segment optical coherence tomography (AS-OCT) is a non-contact method analogous to ultrasound biomicroscopy (UBM) for the evaluation of the anterior segment structure with a higher resolution that uses infrared instead of ultrasound. In this chapter, we discuss the available types of AS-OCT. 1. TD-OCT (time-domain AS-OCT) uses a longer wavelength, which results in less scattering and signal loss and deeper structures such as the limbus or scleral spur and angle can be better visualized. It comprises the following. Visante OCT (Carl Zeiss Meditec, Oberkochen, Germany) Slit-lamp OCT (Heidelberg Engineering GmbH, Heidelberg, Germany). 2. Spectral-domain AS-OCT (SD-OCT) or Fourier-domain OCT (FD-OCT) uses a higher speed and shorter wavelength than TD-OCT, which results in better axial resolution and a lesser depth evaluation and limited horizontal scan width. It comprises the following. Spectralis (Heidelberg Engineering GmbH, Heidelberg, Germany) RTVue (Optovue, Inc., California, USA) Cirrus OCT (Carl Zeiss Meditec, Oberkochen, Germany) 3. Swept-source OCT (SS-OCT), which comprises: Casia SS-1000 OCT (Tomey, Nagoya, Japan) DRI OCT-Triton (Topcon Medical Systems) 4. Ultrahigh-resolution OCT (UHR-OCT), which uses a broad-bandwidth light source and more advanced spectrometer and comprises: Bioptigen Envisu (Bioptigen Inc., Research Triangle Park, NC, USA) SOCT Copernicus HR (Optopol Technologies SA, Zawiercie, Poland).

(1) Basics and principles of optical coherence tomography (OCT), which briefly discuss the mechanisms and operation of OCT systems and a comparison of old and new systems (time domain vs. spectral domain) and their reproducibility. It concisely explains the swept-source OCT mechanism and OCT angiography as well. (2) Normal OCT, which describes normal findings and variations that are expected on normal OCT images, and the layers of the normal retina and in different parts of the posterior segment. (3) Enhanced-depth imaging (EDI)-OCT and its applications and indications in various diseases such as choroidal tumors, age-related macular degeneration, diabetic retinopathy, central serous chorioretinopathy, glaucoma, intraocular inflammation, and myopia. Moreover, choroidal measurement and its variations under different conditions are discussed. (4) Limitations and indications of OCT, which evaluate and explain the drawbacks and advantages of this diagnostic method for the exploration of ocular pathologies. (5) Pitfalls and artifacts, which covers and illustrates diagnostic pitfalls and artifacts in OCT image interpretation in circumstances such as the presence of an epiretinal membrane and myopia.

This paper presents a new fully automatic algorithm for classification of 3D Optical Coherence Tomography (OCT) images as Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), and healthy people. The proposed algorithm does not need to any retinal layer alignment and also segmentation processes (e.g., segmentation of intra-retinal layers and lesion structures). The algorithm utilizes a new Wavelet-based Convolutional Mixture of Experts (WCME) model as an adaptive feature extraction and classification method. The WCME benefits from spatial-frequency decomposition and also an ensemble of convolutional neural networks (CNNs) to build a high-level representation of OCT data. In this study, a retinal OCT data set constituted of 148 cases is used for evaluation of the method based on unbiased cross-validation approach. The dataset consists of 50 normal, 50 DME, and 48 AMD OCT acquisitions from Heidelberg device. With the proposed WCME model, the overall algorithm accurately classified the OCT data with a precision rate of 95.21% and an area under the ROC curve (AUC) of 0.986.

This chapter elaborates on the indispensable role of OCT in the management of diabetic retinopathy and retinal vascular diseases. At the beginning, different classifications of diabetic macular edema based on OCT are discussed. Then, various manifestations of diabetic retinopathy such as microaneurysms, hard exudates, hemorrhages, intraretinal microvascular abnormalities (IRMA), and neovascular membranes are discussed by showing their OCT images. One of the hallmarks of diabetes is the pathology of the posterior hyaloid face, which undergoes vitreoschisis and can lead to tractional macular detachment, one of the most visually devastating conditions in diabetes that can only be recognized with OCT in its early stages. A vascular accident superimposed on preexisting diabetic retinopathy is among the challenging issues discussed in this chapter. This, in addition to a variety of vascular accidents and follow-up pictures, is among the topics that have been covered in this chapter. Other valuable information included is OCT features of laser scars, cotton wool spots, lamellar holes, hypertensive retinopathy, retinal macroaneurysm, Purtscher-like retinopathy, and macular telangiectasia.