Exudate detection in color retinal images for mass screening of diabetic retinopathy

International audienceThe automatic detection of exudates in colour eye fundus images is an important task in applications such as diabetic retinopathy screening. The presented work has been undertaken in the framework of the TeleOphta project, whose main objective is to auto-matically detect normal exams in a tele-ophthalmology network, thus reducing the burden on the readers. A new clinical database, e-ophtha EX, containing precisely manually contoured exudates, is introduced. As opposed to previously available databases, e-ophtha EX is very heterogeneous. It contains images gathered within the OPHDIAT telemedicine network for diabetic retinopathy screening. Image definition, quality, as well as patients condition or the retinograph used for the acquisition, for example, are subject to important changes between different examinations. The proposed exudate detection method has been designed for this complex situation. We propose new preprocessing methods, which perform not only normalization and denoising tasks, but also de-tect reflections and artifacts in the image. A new candidates segmentation method, based on mathematical morphology, is proposed. These candidates are characterized using classical features, but also novel contextual features. Finally, a random forest algorithm is used to detect the exudates among the candidates. The method has been validated on the e-ophtha EX database, obtaining an AUC of 0.95. It has been also validated on other databases, obtaining an AUC between 0.93 and 0.95, outperforming state-of-the-art methods

Reduced vessel density in the superficial and deep plexuses in diabetic retinopathy is associated with structural changes in corresponding retinal layers.

PurposeTo explore the relationships between vessel density (VD) in the retinal vascular plexuses with the thickness and structural changes of their corresponding retinal layers in patients with diabetic retinopathy (DR).MethodsRetrospective analysis of 17 eyes of 17 Type 1 diabetes (T1D) patients with severe non-proliferative or proliferative DR and no current or past macular edema. Seventeen age- and sex-matched healthy subjects were included as controls. Using optical coherence tomography (OCT) and OCT-angiography (OCTA), VD was measured in the superficial vascular plexus (SVP) and deep vascular complex (DVC) that includes the intermediate (ICP) and deep capillary plexuses (DCP), and compared to the retinal thickness (RT) of the inner (from the inner limiting membrane to the inner plexiform layer) and intermediate (inner nuclear and outer plexiform layer) retinal layers. The correlation between the inner and intermediate RT and the VD of the corresponding vascular networks (SVP and DVC, respectively) was assessed. All OCT and OCTA examinations were performed using the RTVue XR Avanti (Optovue, Fremont, CA).ResultsThe inner RT and VD in all plexuses were significantly reduced in T1D patients compared to healthy subjects. The capillary drop-out patterns were polygonal and well-defined in the SVP while the ICP and DCP showed a more diffuse capillary rarefaction and a VD that varied in the same proportion. The inner RT significantly correlated with VD in the SVP (r = 0.71 in healthy subjects and r = 0.62 in T1D patients, p ConclusionsIn T1D subjects, OCTA allowed observing different capillary drop-out patterns in the SVP and in the ICP-DCP, with different structural changes in the corresponding retinal layers, suggesting that they should be considered as distinct anatomical and functional entities

Efficient estimation of eye fundus color image quality with convolutional neural networks

International audiencePurpose: Telemedicine networks are being established in several countries for mass screening of retinal pathologies, like diabetic retinopathy or age-related macular degeneration. Images are acquired by trained technicians, using different fundus cameras models. The quality of the resulting images could be insufficient for interpretation by ophthalmologists or automated systems. With the use of hand- held retinographs, this problem will only worsen.An efficient method is presented to estimate the quality of eye fundus images using a relatively simple convolutional neural network. One of the objectives of the method is to give feed-back to the user during acquisition, so that an image can be re-acquired if the quality is too low for image interpretation. The method is based on the estimation of the visibility of the fovea and surrounding vessels.Methods: 6098 images have been extracted from the e-ophtha database, provided by the OPHDIAT telemedecine network. When the fovea and surrounding vessels were considered visible, the center of the fovea was marked on those images. Pre-processing includes image subsamplingof the green channel to 128x128, as our tests have shown that this resolution is good enough for the task. The method uses a purely convolutional neural network, simple enough in order to speed-up prediction and reduce energy consumption. The network learns to predict a 20 pixel diameter disk centered on the fovea, when visible, or nothing, when not visible. A post-processing step based on mathematical morphology gives the final segmentation result. If a single connected component is predicted by the network, its centroid is considered as the center of the fovea.Results: The accuracy of the method is 96.4%, and it correctly identifies ungradable images in 98,7% cases. The precision of the fovea position, when detected, is measured on our database, as well as on the Aria database. The mean test errors are respectively equal to 0.95 and 1.4 pixels, and the maximal errors equal to 4.85 and 6 pixels.Conclusion: The presented method paves the way towards the deployment of embedded quality estimation of eye fundus color images and decreases the number of ungradable images.This work was funded by the French ”Fonds Unique Interministériel” through the Retinoptic project and supported by the Medicen and Systematic competitive clusters

Fast macula detection and application to retinal image quality assessment

International audienceIn this article, we present a segmentation algorithm for assessing retinal image quality with respect to the visibility of the macular region. An image is considered of acceptable quality if the macular region is clearly visible and entirely in the field of view. Additionally, for acceptable images, the method is able to locate the fovea with a maximal error of 0.34 mm. The algorithm is based on a lightweight fully-convolutional network, several thousand times smaller than state-of-the-art networks investigated so far in preliminary studies. We obtain near-human performance for assessing macula visibility and fovea localization. The presented method can easily be embedded in tabletop or handheld retinographs, decreasing the number of ungradable images, saving both patient and physician time. It is an important step towards automatic screening of retinal pathologies, including diabetic retinopathy, which is a major global healthcare issue

Frequency Selective Height-from-Stereo Reconstruction : Application to Retinal Oedemas Detection

International audienc

Hemoglobin A1c and Fasting Plasma Glucose Levels as Predictors of Retinopathy at 10 Years: The French DESIR Study.: Baseline HbA1c,fasting glucose and 10-year retinopathy

International audienceOBJECTIVE: To evaluate the predictive values of hemoglobin A(1c) (HbA(1c)) and fasting plasma glucose (FPG) for retinopathy 10 years after the baseline examination. METHODS: Seven hundred men and women from the DESIR (Data From an Epidemiological Study on the Insulin Resistance Syndrome) Study underwent evaluation for retinopathy using a nonmydriatic digital camera. During the preceding 9 years, 235 had diabetes mellitus (treated or FPG level of ≥126 mg/dL at least once), 227 had an impaired FPG level (110-125 mg/dL) at least once, and 238 always had glucose levels within reference limits (<110 mg/dL). RESULTS: Compared with those without retinopathy, the 44 participants with retinopathy at 10 years had higher baseline mean (SD) levels of FPG (130 [49] vs 106 [22] mg/dL) and HbA(1c) (6.4% [1.6%] vs 5.7% [0.7%]) (both, P < .001). The frequency of retinopathy at 10 years, standardized according to the distribution of glycemia across the entire DESIR population, was 3.6%. In our population, FPG levels of 108 and 116 mg/dL had positive predictive values of 8.4% and 14.0%, respectively, for retinopathy at 10 years; HbA(1c) levels of 6.0% and 6.5% had positive predictive values of 6.0% and 14.8%, respectively. After 10 years of follow-up, retinopathy was equally frequent in participants with impaired FPG levels and in those who became diabetic during the study (8.6% and 6.7%, respectively), lower than in those with diabetes at baseline (13.9%). CONCLUSION: Because the positive predictive values for retinopathy increase sharply from 108 mg/dL for FPG and from 6.0% for HbA(1c) levels, these thresholds are proposed to identify those at risk of retinopathy 10 years later