Topical Interferon Alpha 2b in the Treatment of Refractory Diabetic Macular Edema

Purpose: To report the efficacy of topical interferon alpha 2b in the treatment of refractory diabetic macular edema. Methods: In this retrospective interventional case series, five eyes of three individuals with diabetic macular edema resistant to multiple intravitreal injections of anti-vascular endothelial growth factor drugs and macular photocoagulation were included. Results: All studied eyes had undergone multiple intravitreal injections including bevacizumab, combination of bevacizumab and triamcinolone and aflibercept, and macular laser photocoagulation before being included in this study. Two intravitreal ranibizumab injections had also been performed in both eyes of one patient. Two eyes had undergone pars plana vitrectomy, one for diabetic macular edema and the other for rhegmatogenous retinal detachment. After a discussion regarding the experimental topical interferon alpha 2b treatment, all patients agreed to start interferon alpha 2b drops four times a day. One month after the treatment, optical coherence tomography demonstrated a significant improvement in macular structure and thickness which was stable or improved at the three-month follow-up visit. Visual acuity in all eyes was stable or improved throughout the three-month follow-up period. Conjunctival injection and follicular conjunctivitis were the side effects of topical interferon alpha 2b and were treated with lubrication and steroids. Conclusion: This case series demonstrated the potential efficacy of interferon alpha 2b in the treatment of refractory diabetic macular edema. It might be an option in patients with contraindications for intravitreal injections

Optical Coherence Tomographic Findings in Highly Myopic Eyes

Optical coherence tomography (OCT) has enhanced our understanding of changes in different ocular layers when axial myopia progresses and the globe is stretched. These findings consist of dehiscence of retinal layers known as retinoschisis, paravascular inner retinal cleavage, cysts and lamellar holes, peripapillary intrachoroidal cavitation, tractional internal limiting membrane detachment, macular holes (lamellar and full thickness), posterior retinal detachment, and choroidal neovascular membranes. In this review, recent observations regarding retinal changes in highly myopic eyes explored by OCT are described to highlight structural findings that cannot be diagnosed by simple ophthalmoscopy

Multi-scale convolutional neural network for automated AMD classification using retinal OCT images

BACKGROUND AND OBJECTIVE: Age-related macular degeneration (AMD) is the most common cause of blindness in developed countries, especially in people over 60 years of age. The workload of specialists and the healthcare system in this field has increased in recent years mainly due to three reasons: 1) increased use of retinal optical coherence tomography (OCT) imaging technique, 2) prevalence of population aging worldwide, and 3) chronic nature of AMD. Recent advancements in the field of deep learning have provided a unique opportunity for the development of fully automated diagnosis frameworks. Considering the presence of AMD-related retinal pathologies in varying sizes in OCT images, our objective was to propose a multi-scale convolutional neural network (CNN) that can capture inter-scale variations and improve performance using a feature fusion strategy across convolutional blocks. METHODS: Our proposed method introduces a multi-scale CNN based on the feature pyramid network (FPN) structure. This method is used for the reliable diagnosis of normal and two common clinical characteristics of dry and wet AMD, namely drusen and choroidal neovascularization (CNV). The proposed method is evaluated on the national dataset gathered at Hospital (NEH) for this study, consisting of 12649 retinal OCT images from 441 patients, and the UCSD public dataset, consisting of 108312 OCT images from 4686 patients. RESULTS: Experimental results show the superior performance of our proposed multi-scale structure over several well-known OCT classification frameworks. This feature combination strategy has proved to be effective on all tested backbone models, with improvements ranging from 0.4% to 3.3%. In addition, gradual learning has proved to be effective in improving performance in two consecutive stages. In the first stage, the performance was boosted from 87.2%±2.5% to 92.0%±1.6% using pre-trained ImageNet weights. In the second stage, another performance boost from 92.0%±1.6% to 93.4%±1.4% was observed as a result of fine-tuning the previous model on the UCSD dataset. Lastly, generating heatmaps provided additional proof for the effectiveness of our multi-scale structure, enabling the detection of retinal pathologies appearing in different sizes. CONCLUSION: The promising quantitative results of the proposed architecture, along with qualitative evaluations through generating heatmaps, prove the suitability of the proposed method to be used as a screening tool in healthcare centers assisting ophthalmologists in making better diagnostic decisions

Introduction to Optical Coherence Tomography

(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 frequency domain OCT, multiple reference OCT and hand held. (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) Anterior segment OCT, which describe the mechanism and clinical applications in various disease (4) 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. (5) OCT angiography, which explains the mechanism and Clinical application and limitations of OCTA (6) Limitations and indications of OCT, which evaluate and explain the drawbacks and advantages of this diagnostic method for the exploration of ocular pathologies. (7) 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 (8) Artificial intelligence in OCT image analysis, which describes the classification and image synthesis and enhancement

Agreement of Two Different Spectral Domain Optical Coherence Tomography Instruments for Retinal Nerve Fiber Layer Measurements

Purpose: To determine the agreement between Spectralis and Cirrus spectral domain optical coherence tomography (SD-OCT) measurements of peripapillary retinal nerve fiber layer (RNFL) thickness. Methods: Suspected or confirmed cases of glaucoma who met the inclusion criteria underwent peripapillary RNFL thickness measurement using both the Spectralis and Cirrus on the same day within a few minutes. Results: Measurements were performed on 103 eyes of 103 patients with mean age of 50.4±17.7 years. Mean RNFL thickness was 89.22±15.87 versus 84.54±13.68 μm using Spectralis and Cirrus, respectively. The difference between measurements and the average of paired measurements with the two devices showed a significant linear relationship. Bland-Altman plots demonstrated that Spectralis thickness values were systematically larger than that of Cirrus. Conclusion: Spectralis OCT generates higher peripapillary RNFL thickness readings as compared to Cirrus OCT; this should be kept in mind when values obtained with different instruments are compared during follow-up

Segmentation of Choroidal Boundary in Enhanced Depth Imaging OCTs Using a Multiresolution Texture Based Modeling in Graph Cuts

The introduction of enhanced depth imaging optical coherence tomography (EDI-OCT) has provided the advantage of in vivo cross-sectional imaging of the choroid, similar to the retina, with standard commercially available spectral domain (SD) OCT machines. A texture-based algorithm is introduced in this paper for fully automatic segmentation of choroidal images obtained from an EDI system of Heidelberg 3D OCT Spectralis. Dynamic programming is utilized to determine the location of the retinal pigment epithelium (RPE). Bruch's membrane (BM) (the blood-retina barrier which separates the RPE cells of the retina from the choroid) can be segmented by searching for the pixels with the biggest gradient value below the RPE. Furthermore, a novel method is proposed to segment the choroid-sclera interface (CSI), which employs the wavelet based features to construct a Gaussian mixture model (GMM). The model is then used in a graph cut for segmentation of the choroidal boundary. The proposed algorithm is tested on 100 EDI OCTs and is compared with manual segmentation. The results showed an unsigned error of 2.48 ± 0.32 pixels for BM extraction and 9.79 ± 3.29 pixels for choroid detection. It implies significant improvement of the proposed method over other approaches like k-means and graph cut methods

Convolutional mixture of experts model: A comparative study on automatic macular diagnosis in retinal optical coherence tomography imaging

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 for researchers in the field. Methods: This study is designed in order to present a comparative analysis 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 (ME-CNN), 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

Speckle Noise Reduction in Optical Coherence Tomography Using Two-dimensional Curvelet-based Dictionary Learning

The process of interpretation of high-speed optical coherence tomography (OCT) images is restricted due to the large speckle noise. To address this problem, this paper proposes a new method using two-dimensional (2D) curvelet-based K-SVD algorithm for speckle noise reduction and contrast enhancement of intra-retinal layers of 2D spectral-domain OCT images. For this purpose, we take curvelet transform of the noisy image. In the next step, noisy sub-bands of different scales and rotations are separately thresholded with an adaptive data-driven thresholding method, then, each thresholded sub-band is denoised based on K-SVD dictionary learning with a variable size initial dictionary dependent on the size of curvelet coefficients' matrix in each sub-band. We also modify each coefficient matrix to enhance intra-retinal layers, with noise suppression at the same time. We demonstrate the ability of the proposed algorithm in speckle noise reduction of 100 publically available OCT B-scans with and without non-neovascular age-related macular degeneration (AMD), and improvement of contrast-to-noise ratio from 1.27 to 5.12 and mean-to-standard deviation ratio from 3.20 to 14.41 are obtained