Fully automated macular pathology detection in retina optical coherence tomography images using sparse coding and dictionary learning.

Macular oedema is a common complication and vision-limiting factor in uveitis. The aim of this study was to compare retinal thickness as measured by optical coherence tomography and photoreceptor function as measured by fundus-related microperimetry with respect to their correlation with visual acuity. Prospective observational monocentre study. Thirty-one patients (53 eyes) with endogenous uveitis and fluorescein angiographically confirmed macular oedema were evaluated. Foveal thickness was analysed using spectral-domain (Spectralis(TM) ; Heidelberg Engineering, Heidelberg, Germany) OCT and retinal sensitivity was assessed using fundus-related microperimetry (MP1; Nidek Technologies, Padova, Italy). All findings were correlated with best-corrected visual acuity (BCVA). Foveal thickness was correlated with BCVA [p = 0.005, r = 0.38, 95% confidence interval (CI) 0.12-0.59]. For microperimetry measurements, a negative correlation with logMAR visual acuity was found. Fixation abnormalities were not associated with poor visual acuity, increased foveal thickness or retinal sensitivity. In eyes with cystoid changes in the outer plexiform and inner nuclear layer, foveal thickness was increased (p < 0.0001). Epiretinal membrane formation was present in 70%. In these eyes, foveal thickness was significantly increased (p = 0.003) and visual acuity was worse (p = 0.08). Foveal thickness and fundus-related microperimetry were correlated with visual acuity. Cystoid changes in the outer plexiform and inner nuclear layer and the presence of epiretinal membrane were associated with poor visual acuity. Fixation abnormalities were not associated with poor visual acuity.

High-Definition and 3-dimensional Imaging of Macular Pathologies with High-speed Ultrahigh-Resolution Optical Coherence Tomography

RETRACTED: Deep learning network (DL-Net) based classification and segmentation of multi-class retinal diseases using OCT scans

Computer aided diagnosis of diabetic macular edema in retinal fundus and OCT images: A review

Classifying retinal diseases is a complex problem because the early problematic areas of retinal disorders are quite small and conservative. In recent years, Transformer architectures have been successfully applied to solve various retinal related health problems. Age-related macular degeneration (AMD) and diabetic macular edema (DME), two prevalent retinal diseases, can cause partial or total blindness. Diseases therefore require an early and accurate detection. In this study, we proposed Vision Transformer (ViT), Tokens-To-Token Vision Transformer (T2T-ViT) and Mobile Vision Transformer (Mobile-ViT) algorithms to detect choroidal neovascularization (CNV), drusen, and diabetic macular edema (DME), and normal using optical coherence tomography (OCT) images. The predictive accuracies of ViT, T2T-ViT and Mobile-ViT achieved on the dataset for the classification of OCT images are 95.14%, 96.07% and 99.17% respectively. Experimental results obtained from ViT approaches showed that Mobile-ViT have superior performance with regard to classification accuracy in comparison with the others. Overall, it has been observed that ViT architectures have the capacity to classify with high accuracy in the diagnosis of retinal diseases.

Accurate segmentation of pigment epithelial detachment (PED) in retinal optical coherence tomography (OCT) images can help doctors comprehensively analyze and diagnose chorioretinal diseases, such as age-related macular degeneration (AMD), central serous chorioretinopathy and polypoidal choroidal vasculopathy. Due to the serious uneven sizes of PED, some traditional algorithms or common deep networks do not perform well in PED segmentation. In this paper, we propose a novel attention multi-scale network (named as AM-Net) based on a U-shape network to segment PED in OCT images. Compared with the original U-Net, there are two main improvements in the proposed method: (1) Designing channel multiscale module (CMM) to replace the skip-connection layer of the U-Net, which uses channel attention mechanism to obtain multi-scale information. (2) Designing spatial multi-scale module (SMM) based on dilated convolution, which is inserted in the decoder path to make the network pay more attention on the multi-scale spatial information. We evaluated the proposed AM-Net on 240 clinically obtained OCT B-scans with 4-fold cross validation. The mean and standard deviation of Intersection over Union (IoU), Dice Similarity Coefficient (DSC), Sensitivity (Sen) and Specificity (Spe) are 72.12± 9.60%, 79.17±8.25%, 93.05±1.72% and 79.93±5.77%, respectively.

Age-related macular degeneration (AMD) is a complex eye disorder affecting millions worldwide. This article uses deep learning techniques to investigate the relationship between AMD, genetics and optical coherence tomography (OCT) scans. The cohort consisted of 332 patients, of which 235 were diagnosed with AMD and 97 were controls with no signs of AMD. The genome-wide association studies summary statistics utilized to establish the polygenic risk score (PRS) in relation to AMD were derived from the GERA European study. A PRS estimation based on OCT volumes for both eyes was performed using a proprietary convolutional neural network (CNN) model supported by machine learning models. The method's performance was assessed using numerical evaluation metrics, and the Grad-CAM technique was used to evaluate the results by visualizing the features learned by the model. The best results were obtained with the CNN and the Extra Tree regressor (MAE = 0.55, MSE = 0.49, RMSE = 0.70, R2 = 0.34). Extending the feature vector with additional information on AMD diagnosis, age and smoking history improved the results slightly, with mainly AMD diagnosis used by the model (MAE = 0.54, MSE = 0.44, RMSE = 0.66, R2 = 0.42). Grad-CAM heatmap evaluation showed that the model decisions rely on retinal morphology factors relevant to AMD diagnosis. The developed method allows an efficient PRS estimation from OCT images. A new technique for analysing the association of OCT images with PRS of AMD, using a deep learning approach, may provide an opportunity to discover new associations between genotype-based AMD risk and retinal morphology.

In this paper we propose a technique for obtaining a high resolution (HR) image from a single low resolution (LR) image -using joint learning dictionary - on the basis of image statistic research. It suggests that with an appropriate choice of an over-complete dictionary, image patches can be well represented as a sparse linear combination. Medical imaging for clinical analysis and medical intervention is being used for creating visual representations of the interior of a body, as well as visual representation of the function of some organs or tissues (physiology). A number of medical imaging techniques are in use like MRI, CT scan, X-rays and Optical Coherence Tomography (OCT). OCT is one of the new technologies in medical imaging and one of its uses is in ophthalmology where it is being used for analysis of the choroidal thickness in the eyes in healthy and disease states such as age-related macular degeneration, central serous chorioretinopathy, diabetic retinopathy and inherited retinal dystrophies. We have proposed a technique for enhancing the OCT images which can be used for clearly identifying and analyzing the particular diseases. Our method uses dictionary learning technique for generating a high resolution image from a single input LR image. We train two joint dictionaries, one with OCT images and the second with multiple different natural images, and compare the results with previous SR technique. Proposed method for both dictionaries produces HR images which are comparatively superior in quality with the other proposed method of SR. Proposed technique is very effective for noisy OCT images and produces up-sampled and enhanced OCT images.

Optical coherence tomography (OCT) is becoming one of the most important detection modalities for fast and noninvasive assessment of ophthalmological diseases. Diabetic macular edema (DME) is one of the important reasons leads to blindness. Its pathological features are mainly manifested in the accumulation of fluid in the retina. An automated method is proposed to identify and quantify the volume of cystoid macular edema in Spectral Domain OCT (SD-OCT) images. In the first stage of preprocessing, we balance the apparent signal-to-noise of each retinal OCT image. Because the signal-to-noise of OCT images is variable from patient to patient, and balance of the signal-to-noise ensures consistent segmentation of cystoid fluid. Speckle noise is the main reason leads to quality degrading in OCT images. The denoising method should be efficient for the noise suppression, and the edge information can be preserved at the same time. Then we used the anisotropic diffusion filter to suppress shot noise. The intensity inhomogeneity in OCT images may lead to false detection in the further segmentation work. Then we used the gamma transformation to change the brightness, which eliminates the effect availably. In the second stage of segmentation, we solve the problem of segmentation effectively by the improved level set method and calculated the area of edema area, which provides quantitative analytic tools for clinical diagnosis and therapy. Finally, the proposed method was evaluated on 15 SD-OCT retinal images from DME adults. Leave-one-out evaluation resulted in a precision, sensitivity and dice similarity coefficient (DSC) of 81.12%, 86.90% and 80.05%, respectively.

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Age-related macular degeneration (ARMD) is a degenerative eye disease among people over the age of 50. This disorder affects the retina and macula of the eye. The macula is the central part of the retina. The function of the macular region is to give a sharp and clear vision. ARMD is mainly identified by the presence of yellow deposits called drusen and the structural changes in the retinal pigment epithelium (RPE) layer of the retina. If ARMD is not identified at an early stage, it will lead to permanent blindness. The ARMD is mainly diagnosed using Color fundus and Optical coherence tomography (OCT) images of the retina. OCT is an imaging technique that makes use of low-coherence light to capture two-and three-dimensional images from within optical scattering media. OCT images usually are at a micrometer resolution, affected by a noise called speckle noise. If speckle noise is present in an OCT image, diagnosis of ARMD is very difficult, irrespective of whether it is manually or automatically. So, removal of speckle-noise is significant for the accurate diagnosis and early detection of ARMD. In this paper, we evaluate the performance of eight popular image noise reduction filters for its ability to remove speckle noise in terms of different performance measures on a publicly available OCT dataset. Our evaluation shows that, out of the filters selected for assessment, a fast non-local mean filter outperforms all other filters regarding all the performance measures used.

What Is the Issue?&#x0D; &#x0D; Wet age-related macular degeneration (AMD) is a serious health concern that causes reduced vision-related function, poor overall quality of life, and increased health care resource usage.&#x0D; Anti-vascular endothelial growth factor (VEGF) drugs are the first-line treatment for wet AMD, and they are injected into the eye. These drugs can slow disease progression but are expensive and require trained medical professionals to administer and monitor treatment response.&#x0D; The criteria for initial injection, maintenance, and discontinuation of anti-VEGF drugs in patients with wet AMD remain unclear.&#x0D; &#x0D; What Did We Do?&#x0D; &#x0D; To inform decisions about the appropriate use of anti-VEGF drugs to treat adults with wet AMD, CADTH sought to identify and summarize recommendations about criteria for the diagnosis of wet AMD and best practices for subsequent treatment with anti-VEGF drugs.&#x0D; An information specialist searched the peer-reviewed and grey literature for clinical practice guidelines published since 2018.&#x0D; &#x0D; What Did We Find?&#x0D; &#x0D; Three evidence-based guidelines recommend optical coherence tomography (OCT) to assess patients with suspected wet AMD. The National Institute for Health and Care Excellence (NICE) guidelines then strongly recommend fundus fluorescein angiography only when OCT does not exclude neovascular disease.&#x0D; The evidence-based guidelines recommend anti-VEGF drugs for wet or neovascular AMD in adults, particularly for patients with recent disease progression. One guideline suggests starting anti-VEGF treatment as soon as possible once the diagnosis of wet AMD is made.&#x0D; The NICE guideline suggests that there may not be any clinically significant distinctions in the effectiveness and safety of various anti-VEGF treatments, including aflibercept, bevacizumab, and ranibizumab, indicating a need for an individualized approach when selecting specific anti-VEGF drugs. However, 1 guideline is inconsistent with NICE, as it suggests choosing bevacizumab first.&#x0D; During the maintenance phase of using anti-VEGF drugs, it is important to closely monitor the patient's response and the disease activity and adjust the injection intervals accordingly.&#x0D; Guidelines recommend taking an individualized approach and emphasizing active patient involvement in switching and discontinuing anti-VEGF drugs for adults with wet AMD.&#x0D; Guidelines or guidance documents without a literature search or unclear methods provided generally consistent recommendations with the included evidence-based guidelines.&#x0D; We did not identify any evidence-based guidelines regarding the use of faricimab or brolucizumab. However, NICE published technology appraisals supporting the use of recently approved anti-VEGF drugs (faricimab and brolucizumab) as alternative options for patients who meet similar criteria for treatment with bevacizumab or aflibercept, and under agreed pricing conditions.&#x0D; &#x0D; What Does It Mean?&#x0D; &#x0D; All identified guidelines consistently recommend anti-VEGF drugs for adults with wet active AMD, with some providing guidance on the use of OCT and other ophthalmological assessments to identify criteria for starting and adjusting treatment.&#x0D; These guidelines recommend personalized and patient-centred approaches for selecting, switching, and discontinuing anti-VEGF medications. Cost is also a crucial factor in decision-making, especially for aflibercept, ranibizumab, and recently approved anti-VEGF drugs such as faricimab and brolucizumab.&#x0D; Further research and evidence-based guidelines are necessary to confirm whether newer drugs can be used in similar situations as older drugs.&#x0D;

New Examination Methods for Macular Disorders: Application of Diagnosis and Treatment

Background/aimsAflibercept is an approved therapy for neovascular age-related macular degeneration (AMD) and diabetic macular oedema (DME). In vitro and in vivo studies did not detect toxicity to the retinal pigment...

Multi-scale deep feature fusion for automated classification of macular pathologies from OCT images