Automated diabetic retinopathy detection using optical coherence tomography angiography: a pilot study.

BACKGROUND: Optical coherence tomography angiography (OCTA) is increasingly being used to evaluate diabetic retinopathy, but the interpretation of OCTA remains largely subjective. The purpose of this study was to design a computer-aided diagnostic (CAD) system to diagnose non-proliferative diabetic retinopathy (NPDR) in an automated fashion using OCTA images. METHODS: This was a two-centre, cross-sectional study. Adults with type II diabetes mellitus (DMII) were eligible for inclusion. OCTA scans of the macula were taken, and the five vascular maps generated per eye were analysed by a novel CAD system. For the purpose of classification/diagnosis, three different local features-blood vessel density, blood vessel calibre and the size of the foveal avascular zone (FAZ)-were segmented from these images and used to train a new, automated classifier. RESULTS: One hundred and six patients with DMII were included in the study, 23 with no DR and 83 with mild NPDR. When using features of the superficial retinal map alone, the system demonstrated an accuracy of 80.0% and area under the curve (AUC) of 76.2%. Using the features of the deep retinal map alone, accuracy was 91.4% and AUC 89.2%. When data from both maps were combined, the presented CAD system demonstrated overall accuracy of 94.3%, sensitivity of 97.9%, specificity of 87.0%, area under curve (AUC) of 92.4% and dice similarity coefficient of 95.8%. CONCLUSION: Automated diagnosis of NPDR using OCTA images is feasible and accurate. Combining this system with OCT data is a plausible next step that would likely improve its robustness.

Classification of retinal diseases based on OCT Images.

Optical Coherence Topography (OCT) is an emerging biomedical imaging technology that offers non-invasive real-time, high-resolution imaging of highly scattering tissues. It is widely used in ophthalmology to perform diagnostic imaging on the structure of the anterior eye and the retina. Clinical studies are carried out to assess the application of OCT for some retinal diseases. OCT can provide means for early detection for various types of diseases because morphological changes often occur before the physical symptoms of these diseases. In addition, follow-up imaging can assess treatment effectiveness and recurrence of a disease. A review in this area is needed to identify the results and the findings from OCT images in the field of retinal diseases and how to use these findings to help in clinical applications. This paper overviews the current techniques that are developed to determine the ability of OCT images for early detection/diagnosis of retinal diseases. Also, the paper remarks several challenges that face the researchers in the analysis of the OCT retinal images.

Retracted: Early diabetic retinopathy diagnosis based on local retinal blood vessels analysis in optical coherence tomography angiography (OCTA) images.

The above article from Medical Physics, published online on 22 February 2018 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the authors, the journal Editor in Chief and John Wiley & Sons Ltd. The retraction has been agreed following an investigation carried out by the editors due to major overlap with a previously published article: British Journal of Ophthalmology (BJO) (Sandhu HS, Eladawi N, Elmogy M, et al Automated diabetic retinopathy detection using optical coherence tomography angiography: a pilot study, British Journal of Ophthalmology Published Online First: 23 January 2018. doi:10.1136/bjophthalmol-2017-311489.

Early diabetic retinopathy diagnosis based on local retinal blood vessel analysis in optical coherence tomography angiography (OCTA) images.

PURPOSE: This paper introduces a new computer-aided diagnosis (CAD) system for detecting early-stage diabetic retinopathy (DR) using optical coherence tomography angiography (OCTA) images. METHODS: The proposed DR-CAD system is based on the analysis of new local features that describe both the appearance and retinal structure in OCTA images. It starts with a new segmentation approach that has the ability to extract the blood vessels from superficial and deep retinal OCTA maps. The high capability of our segmentation approach stems from using a joint Markov-Gibbs random field stochastic model integrating a 3D spatial statistical model with a first-order appearance model of the blood vessels. Following the segmentation step, three new local features are estimated from the segmented vessels and the foveal avascular zone (FAZ): (a) vessels density, (b) blood vessel calibre, and (c) width of the FAZ. To distinguish mild DR patients from normal cases, the estimated three features are used to train and test a support vector machine (SVM) classifier with the radial basis function (RBF) kernel. RESULTS: On a cohort of 105 subjects, the presented DR-CAD system demonstrated an overall accuracy (ACC) of 94.3%, a sensitivity of 97.9%, a specificity of 87.0%, the area under the curve (AUC) of 92.4%, and a Dice similarity coefficient (DSC) of 95.8%. This in turn demonstrates the promise of the proposed CAD system as a supplemental tool for early detection of DR. CONCLUSION: We developed a new DR-CAD system that is capable of diagnosing DR in its early stage. The proposed system is based on extracting three different features from the segmented OCTA images, which reflect the changes in the retinal vasculature network.

Automatic blood vessels segmentation based on different retinal maps from OCTA scans.

The retinal vascular network reflects the health of the retina, which is a useful diagnostic indicator of systemic vascular. Therefore, the segmentation of retinal blood vessels is a powerful method for diagnosing vascular diseases. This paper presents an automatic segmentation system for retinal blood vessels from Optical Coherence Tomography Angiography (OCTA) images. The system segments blood vessels from the superficial and deep retinal maps for normal and diabetic cases. Initially, we reduced the noise and improved the contrast of the OCTA images by using the Generalized Gauss-Markov random field (GGMRF) model. Secondly, we proposed a joint Markov-Gibbs random field (MGRF) model to segment the retinal blood vessels from other background tissues. It integrates both appearance and spatial models in addition to the prior probability model of OCTA images. The higher order MGRF (HO-MGRF) model in addition to the 1st-order intensity model are used to consider the spatial information in order to overcome the low contrast between vessels and other tissues. Finally, we refined the segmentation by extracting connected regions using a 2D connectivity filter. The proposed segmentation system was trained and tested on 47 data sets, which are 23 normal data sets and 24 data sets for diabetic patients. To evaluate the accuracy and robustness of the proposed segmentation framework, we used three different metrics, which are Dice similarity coefficient (DSC), absolute vessels volume difference (VVD), and area under the curve (AUC). The results on OCTA data sets (DSC=95.04±3.75%, VVD=8.51±1.49%, and AUC=95.20±1.52%) show the promise of the proposed segmentation approach.