Classification Of Macular Edema Research Articles

Development of Clinical and Morphometric Classification of Macular Edema in Retinal Vein Occlusions

Purpose. To study the morphometric features of macular edema in occlusions of the retinal veins, to determine and systematize the conditional gradations of the degrees of its severity.Patients and methods. 67 patients (67 eyes) were recruited using a continuous sampling method. Their age ranged from 38 to 84 years (on average 60.0 ± 10.5 years). Among them there were 27 men and 41 women. In 19 patients, there was an occlusion of the central retinal vein (28 %), in 48 people — occlusion of its branches (72 %). The lesions of the superior temporal branch prevailed — 32 people (65 %), 16 patients had occlusion of the inferior temporal branch (35 %). Systematization of the obtained data was carried out with the allocation of morphometric gradations of the severity of macular edema against the background of retinal vein occlusions.Results. According to our data, the morphometric parameters of the macular retina in the presence of macular edema were characterized by a wide range of gradations in the thickness of the foveal retina and the volume of the macular retina (from 239 to 861 μm and from 10,4 to 17,4 mm3 respectively).The morphological features of macular edema in retinal vein occlusions are the formation of cystic cavities in the outer and inner layers of the retina, convex deformation of the retinal profile, and the development of neuroepithelial detachment in 21 % of cases. Taking into account the data obtained, we proposed a conditional morphometric classification of the severity of macular edema in retinal vein occlusions. A mild degree of macular edema was characterized by values of the foveal retinal thickness index up to 400 μm; for medium — from 401 to 600 microns; for severe macular edema — over 600 microns. In accordance with our own conditional clinical morphometric classification of the severity of macular edema, mild macular edema was determined in 32 %, medium degree — in 43 %, severe — in 25 % of patients’ eyes.Conclusion. The identification of various clinical and morphometric degrees of severity of macular edema, in our opinion, is of practical importance, since it will allow more optimal and objective selection of the most optimal treatment tactics, taking into account the individual morphometric characteristics of the patient’s macular region.

Deep Ensemble Learning Based Objective Grading of Macular Edema by Extracting Clinically Significant Findings from Fused Retinal Imaging Modalities.

Macular edema (ME) is a retinal condition in which central vision of a patient is affected. ME leads to accumulation of fluid in the surrounding macular region resulting in a swollen macula. Optical coherence tomography (OCT) and the fundus photography are the two widely used retinal examination techniques that can effectively detect ME. Many researchers have utilized retinal fundus and OCT imaging for detecting ME. However, to the best of our knowledge, no work is found in the literature that fuses the findings from both retinal imaging modalities for the effective and more reliable diagnosis of ME. In this paper, we proposed an automated framework for the classification of ME and healthy eyes using retinal fundus and OCT scans. The proposed framework is based on deep ensemble learning where the input fundus and OCT scans are recognized through the deep convolutional neural network (CNN) and are processed accordingly. The processed scans are further passed to the second layer of the deep CNN model, which extracts the required feature descriptors from both images. The extracted descriptors are then concatenated together and are passed to the supervised hybrid classifier made through the ensemble of the artificial neural networks, support vector machines and naïve Bayes. The proposed framework has been trained on 73,791 retinal scans and is validated on 5100 scans of publicly available Zhang dataset and Rabbani dataset. The proposed framework achieved the accuracy of 94.33% for diagnosing ME and healthy subjects and achieved the mean dice coefficient of 0.9019 ± 0.04 for accurately extracting the retinal fluids, 0.7069 ± 0.11 for accurately extracting hard exudates and 0.8203 ± 0.03 for accurately extracting retinal blood vessels against the clinical markings.

Automated diagnosis of macular edema and central serous retinopathy through robust reconstruction of 3D retinal surfaces

Background and objectivesMacular diseases tend to damage macula within human retina due to which the central vision of a person is affected. Macular edema (ME) and central serous retinopathy (CSR) are two of the most common macular diseases. Many researchers worked on automated detection of ME from optical coherence tomography (OCT) and fundus images, whereas few researchers have worked on diagnosing central serous retinopathy. But this paper proposes a fully automated method for the classification of ME and CSR through robust reconstruction of 3D OCT retinal surfaces. MethodsThe proposed system uses structure tensors to extract retinal layers from OCT images. The 3D retinal surface is then reconstructed by extracting the brightness scan (B-scan) thickness profile from each coherent tensor. The proposed system extracts 8 distinct features (3 based on retinal thickness profile of right side, 3 based on thickness profile of left side and 2 based on top surface and cyst spaces within retinal layers) from 30 labeled volumes (10 healthy, 10 CSR and 10 ME) which are used to train the supervised support vector machines (SVM) classifier. ResultsIn this research we have considered 90 OCT volumes (30 Healthy, 30 CSR and 30 ME) of 73 patients to test the proposed system where our proposed system correctly classified 89 out of 90 cases and has promising receiver operator characteristics (ROC) ratings with accuracy, sensitivity and specificity of 98.88%, 100%, and 96.66% respectively. ConclusionThe proposed system is quite fast and robust in detecting all the three types of retinal pathologies from volumetric OCT scans. The proposed system is fully automated and provides an early and on fly diagnosis of ME and CSR syndromes. 3D macular thickness surfaces can further be used as decision support parameter in clinical studies to check the volume of cyst.

Structure tensor based automated detection of macular edema and central serous retinopathy using optical coherence tomography images.

Macular edema (ME) and central serous retinopathy (CSR) are two macular diseases that affect the central vision of a person if they are left untreated. Optical coherence tomography (OCT) imaging is the latest eye examination technique that shows a cross-sectional region of the retinal layers and that can be used to detect many retinal disorders in an early stage. Many researchers have done clinical studies on ME and CSR and reported significant findings in macular OCT scans. However, this paper proposes an automated method for the classification of ME and CSR from OCT images using a support vector machine (SVM) classifier. Five distinct features (three based on the thickness profiles of the sub-retinal layers and two based on cyst fluids within the sub-retinal layers) are extracted from 30 labeled images (10 ME, 10 CSR, and 10 healthy), and SVM is trained on these. We applied our proposed algorithm on 90 time-domain OCT (TD-OCT) images (30 ME, 30 CSR, 30 healthy) of 73 patients. Our algorithm correctly classified 88 out of 90 subjects with accuracy, sensitivity, and specificity of 97.77%, 100%, and 93.33%, respectively.

Is there a correlation between structural alterations and retinal sensitivity in morphological patterns of diabetic macular edema?

Spectral domain optical coherence tomography (SDOCT) enables enhanced visualization of retinal layers and delineation of structural alterations in diabetic macular edema (DME). Microperimetry (MP) is a new technique that allows fundus-related testing of local retinal sensitivity. Combination of these two techniques would enable a structure-function correlation with insights into pathomechanism of vision loss in DME. To correlate retinal structural derangement with retinal sensitivity alterations in cases with diabetic macular edema, using SDOCT and MP. Prospective study of 34 eyes of 30 patients with DME. All patients underwent comprehensive ophthalmic examination, fluorescein angiography, microperimetry and SDOCT. Four distinct morphological patterns of DME were identified- diffuse retinal thickening (DRT), cystoid macular edema (CME), schitic retinal thickening (SRT) and neourosensory detachment (NSD) of fovea. Some retinal loci presented with a mixture of above patterns There was significant difference in retinal thickness between groups (P<0.001). Focal retinal sensitivity measurement revealed relatively preserved retinal sensitivity in areas with DRT (13.8 dB), moderately reduced sensitivity (7.9 dB) in areas with CME, and gross retinal sensitivity loss in areas with SRT (1.2 dB) and NSD (4.7 dB) (P<0.001). Analysis of regional scotoma depth demonstrated similar pattern. Retinal sensitivity showed better correlation to OCT pattern (r=-0.68, P<0.001) than retinal thickness (r=-0.44, P<0.001). Structure-function correlation allows better understanding of the pathophysiology of visual loss in different morphological types of DME. Classification of macular edema into these categories has implications on the prognosis and predictive value of treatment.

Imaging of Diabetic Retinopathy and Diabetic Macular Edema

Imaging of the retinal complications of diabetes mellitus is rapidly changing from the emergence of new technology such as optical coherence tomography. In particular, the characterization of diabetic macular edema is much easier for the clinician and there are new, more sensitive clinical research end points. However, our understanding of structure-functional relationships remains suboptimal and the classification of macular edema by optical coherence tomography continues to evolve. The classification of diabetic retinopathy severity continues to rely upon fundus photography, although the transition from film to digital photography presents both challenges and advantages.