Angiography by Optical Coherence Tomography (OCT-A) is a non-invasive retinal imaging modality of recent appearance that allows the visualization of the vascular structure at predefined depths based on the detection of the blood movement through the retinal vasculature. In this way, OCT-A images constitute a suitable scenario to analyze the retinal vascular properties of regions of interest as is the case of the macular area, measuring the characteristics of the foveal vascular and avascular zones. Extracted parameters of this region can be used as prognostic factors that determine if the patient suffers from certain pathologies (such as diabetic retinopathy or retinal vein occlusion, among others), indicating the associated pathological degree. The manual extraction of these biomedical parameters is a long, tedious and subjective process, introducing a significant intra and inter-expert variability, which penalizes the utility of the measurements. In addition, the absence of tools that automatically facilitate these calculations encourages the creation of computer-aided diagnosis frameworks that ease the doctor’s work, increasing their productivity and making viable the use of this type of vascular biomarkers. In this work we propose a fully automatic system that identifies and precisely segments the region of the foveal avascular zone (FAZ) using a novel ophthalmological image modality as is OCT-A. The system combines different image processing techniques to firstly identify the region where the FAZ is contained and, secondly, proceed with the extraction of its precise contour. The system was validated using a representative set of 213 healthy and diabetic OCT-A images, providing accurate results with the best correlation with the manual measurements of two experts clinician of 0.93 as well as a Jaccard’s index of 0.82 of the best experimental case in the experiments with healthy OCT-A images. The method also provided satisfactory results in diabetic OCT-A images, with a best correlation coefficient with the manual labeling of an expert clinician of 0.93 and a Jaccard’s index of 0.83. This tool provides an accurate FAZ measurement with the desired objectivity and reproducibility, being very useful for the analysis of relevant vascular diseases through the study of the retinal micro-circulation.

Diabetic Retinopathy and Diabetic Macular Edema (DME) represent one of the main causes of blindness in developed countries. They are characterized by fluid deposits in the retinal layers, causing a progressive vision loss over the time. The clinical literature defines three DME types according to the texture and disposition of the fluid accumulations: Cystoid Macular Edema (CME), Diffuse Retinal Thickening (DRT) and Serous Retinal Detachment (SRD). Detecting each one is essential as, depending on their presence, the expert will decide on the adequate treatment of the pathology. In this work, we propose a robust detection and visualization methodology based on the analysis of independent image regions. We study a complete and heterogeneous library of 375 texture and intensity features in a dataset of 356 labeled images from two of the most used capture devices in the clinical domain: a CIRRUS^TM HD-OCT 500 Carl Zeiss Meditec and 179 OCT images from a modular HRA + OCT SPECTRALIS^® from Heidelberg Engineering, Inc. We extracted 33,810 samples for each type of DME for the feature analysis and incremental training of four different classifier paradigms. This way, we achieved an 84.04% average accuracy for CME, 78.44% average accuracy for DRT and 95.40% average accuracy for SRD. These models are used to generate an intuitive visualization of the fluid regions. We use an image sampling and voting strategy, resulting in a system capable of detecting and characterizing the three types of DME presenting them in an intuitive and repeatable way.

The assessment of vascular biomarkers and their correlation with visual acuity is one of the most important issues in the diagnosis and follow-up of retinal vein occlusions (RVOs). The high workloads of clinical practice make it necessary to have a fast, objective, and automatic method to analyze image features and correlate them with visual function. The aim of this study is to propose a fully automatic system which is capable of estimating visual acuity (VA) in RVO eyes, based only on information obtained from macular optical coherence tomography angiography (OCTA) images. We also propose an automatic methodology to rapidly measure the foveal avascular zone (FAZ) area and the vascular density (VD) in the superficial and deep capillary plexuses in swept-source OCTA images centered on the fovea. The proposed methodology is validated using a representative sample of 133 visits of 50 RVO patients. Our methodology estimates VA with very high precision and is even more accurate when we integrate depth information, providing a high correlation index of 0.869 with the real VA, which outperforms the correlation index of 0.855 obtained when estimating VA from the data obtained by the semiautomatic existing method. In conclusion, the proposed method is the first computational system able to estimate VA in RVO, with the additional benefits of being automatic, less time-consuming, objective and more accurate. Furthermore, the proposed method is able to integrate depth information, a feature which is lacking in the existing method.

The Foveal Avascular Zone (FAZ) is a capillary-free area that is placed inside the macula and its morphology and size represent important biomarkers to detect different ocular pathologies such as diabetic retinopathy, impaired vision or retinal vein occlusion. Therefore, an adequate and precise segmentation of the FAZ presents a high clinical interest. About to this, Angiography by Optical Coherence Tomography (OCT-A) is a non-invasive imaging technique that allows the expert to visualize the vascular and avascular foveal zone. In this work, we present a robust methodology composed of three stages to model, localize, and segment the FAZ in OCT-A images. The first stage is addressed to generate two FAZ normality models: superficial and deep plexus. The second one uses the FAZ model as a template to localize the FAZ center. Finally, in the third stage, an adaptive binarization is proposed to segment the entire FAZ region. A method based on this methodology was implemented and validated in two OCT-A image subsets, presenting the second subset more challenging pathological conditions than the first. We obtained localization success rates of 100% and 96% in the first and second subsets, respectively, considering a success if the obtained FAZ center is inside the FAZ area segmented by an expert clinician. Complementary, the Dice score and other indexes (Jaccard index and Hausdorff distance) are used to measure the segmentation quality, obtaining competitive average values in the first subset: 0.84 ± 0.01 (expert 1) and 0.85 ± 0.01 (expert 2). The average Dice score obtained in the second subset was also acceptable (0.70 ± 0.17), even though the segmentation process is more complex in this case.

Optical Coherence Tomography Angiography (OCTA) constitutes a new non-invasive ophthalmic image modality that allows the precise visualization of the micro-retinal vascularity that is commonly used to analyze the foveal region. Given that there are many systemic and eye diseases that affect the eye fundus and its vascularity, the analysis of that region is crucial to diagnose and estimate the vision loss. The Visual Acuity (VA) is typically measured manually, implying an exhaustive and time-consuming procedure. In this work, we propose a method that exploits the information of the OCTA images to automatically estimate the VA with an accurate error of 0.1713.