Search results for "Diabetic Macular Edema"

Œdème maculaire diabétique : diagnostic et bilan pré-thérapeutique

2015

Classification of SD-OCT Volumes for DME Detection: An Anomaly Detection Approach

2016

International audience; Diabetic Macular Edema (DME) is the leading cause of blindness amongst diabetic patients worldwide. It is characterized by accumulation of water molecules in the macula leading to swelling. Early detection of the disease helps prevent further loss of vision. Naturally, automated detection of DME from Optical Coherence Tomography (OCT) volumes plays a key role. To this end, a pipeline for detecting DME diseases in OCT volumes is proposed in this paper. The method is based on anomaly detection using Gaussian Mixture Model (GMM). It starts with pre-processing the B-scans by resizing, flattening, filtering and extracting features from them. Both intensity and Local Binar…

Classification of SD-OCT volumes with multi pyramids, LBP and HOG descriptors: application to DME detections.

2016

This paper deals with the automated detection of Diabetic Macular Edema (DME) on Optical Coherence Tomography (OCT) volumes. Our method considers a generic classification pipeline with preprocessing for noise removal and flattening of each B-Scan. Features such as Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) are extracted and combined to create a set of different feature vectors which are fed to a linear-Support Vector Machines (SVM) Classifier. Experimental results show a promising sensitivity/specificity of 0.75/0.87 on a challenging dataset.

Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography

2019

Background and objectives: Spectral Domain Optical Coherence Tomography (SD-OCT) is a volumetric imaging technique that allows measuring patterns between layers such as small amounts of fluid. Since 2012, automatic medical image analysis performance has steadily increased through the use of deep learning models that automatically learn relevant features for specific tasks, instead of designing visual features manually. Nevertheless, providing insights and interpretation of the predictions made by the model is still a challenge. This paper describes a deep learning model able to detect medically interpretable information in relevant images from a volume to classify diabetes-related retinal d…

Classifying DME vs Normal SD-OCT volumes: A review

2016

International audience; This article reviews the current state of automatic classification methodologies to identify Diabetic Macular Edema (DME) versus normal subjects based on Spectral Domain OCT (SD-OCT) data. Addressing this classification problem has valuable interest since early detection and treatment of DME play a major role to prevent eye adverse effects such as blindness. The main contribution of this article is to cover the lack of a public dataset and benchmark suited for classifying DME and normal SD-OCT volumes, providing our own implementation of the most relevant methodologies in the literature. Subsequently, 6 different methods were implemented and evaluated using this comm…

Classification of SD-OCT Volumes with LBP: Application to DME Detection

2015

International audience; This paper addresses the problem of automatic classification of Spectral Domain OCT (SD-OCT) data for automatic identification of patients with Diabetic Macular Edema (DME) versus normal subjects. Our method is based on Local Binary Patterns (LBP) features to describe the texture of Optical Coherence Tomography (OCT) images and we compare different LBP features extraction approaches to compute a single signature for the whole OCT volume. Experimental results with two datasets of respectively 32 and 30 OCT volumes show that regardless of using low or high level representations, features derived from LBP texture have highly discriminative power. Moreover, the experimen…

Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection

2016

International audience; This paper addresses the problem of automatic classification of Spectral Domain OCT (SD-OCT) data for automatic identification of patients with Diabetic Macular Edema (DME) versus normal subjects. Optical Coherence Tomography (OCT) has been a valuable diagnostic tool for DME, which is among the most common causes of irreversible vision loss in individuals with diabetes. Here, a classification framework with five distinctive steps is proposed and we present an extensive study of each step. Our method considers combination of various pre-processings in conjunction with Local Binary Patterns (LBP) features and different mapping strategies. Using linear and non-linear cl…

Macular laser photocoagulation guided by spectral-domain optical coherence tomography versus fluorescein angiography for diabetic macular edema.

2011

Roberto Gallego-Pinazo1,2, Ana Marina Suelves-Cogollos1, Rosa Dolz-Marco1, J Fernando Arevalo3, Salvador García-Delpech1, J Luis Mullor4, Manuel Díaz-Llopis1,2,51Department of Ophthalmology, Hospital Universitario La Fe, Valencia, Spain; 2Centro de Investigación Biomédica en Red de Enfermedades Raras, Valencia, Spain; 3Retina and Vitreous Service, Clinical Ophthalmology Center, Caracas, Venezuela; 4Unit of Experimental Ophthalmology, Hospital Universitario La Fe, Valencia, Spain; 5University of Valencia, Faculty of Medicine, Valencia, SpainBackground: The aim of this study was to compare the efficacy of spectral-domain optical coherence tomography…

Retina Summit Karlsruhe 2009

2009

During the this year´s Retina Summit Karlsruhe, an international expert faculty of 20 highly renowned retina specialists and more than 300 participants found their way to Karlsruhe to share their knowledge of the latest trends in surgical technology and pharmacology. After an angiography course on Friday, the 1-day symposium of almost 40 short lectures covered recent developments in age-related macular degeneration and diabetic macular edema, and the implications and challenges associated with microincision vitrectomy surgery, new devices and advances in imaging technologies.

A Real-World Study of Dexamethasone Implant in Treatment-Naïve Patients with Diabetic Macular Edema: Efficacy and Correlation Between Inflammatory Bi…

2020

Purpose There has been an increasing clinical interest in specific retinal parameters as non-invasive biomarkers of retinal inflammation in diabetic macular edema (DME) that have been shown to have prognostic value, such as hyperreflective retinal fields (HRFs) and subfoveal neuroretinal detachment (SND). Methods We conducted a prospective, non-comparative study of treatment-naive patients with DME to evaluate the efficacy of a Pro Re Nata (PRN) regimen of intravitreal dexamethasone implant 0.7 mg (DexI, Ozurdex™). After administration, patients underwent subsequent injections according to PRN criteria in case of edema relapse, but not earlier than 4 months after the previous treatment. Pat…