The automatic identification and segmentation of edemas associated with diabetic macular edema (DME) constitutes a crucial ophthalmological issue as they provide useful information for the evaluation of the disease severity. According to clinical knowledge, the DME disorder can be categorized into three main pathological types: serous retinal detachment (SRD), cystoid macular edema (CME), and diffuse retinal thickening (DRT). The implementation of computational systems for their automatic extraction and characterization may help the clinicians in their daily clinical practice, adjusting the diagnosis and therapies and consequently the life quality of the patients. In this context, this paper proposes a fully automatic system for the identification, segmentation and characterization of the three ME types using optical coherence tomography (OCT) images. In the case of SRD and CME edemas, different approaches were implemented adapting graph cuts and active contours for their identification and precise delimitation. In the case of the DRT edemas, given their fuzzy regional appearance that requires a complex extraction process, an exhaustive analysis using a learning strategy was designed, exploiting intensity, texture, and clinical-based information. The different steps of this methodology were validated with a heterogeneous set of 262 OCT images, using the manual labeling provided by an expert clinician. In general terms, the system provided satisfactory results, reaching Dice coefficient scores of 0.8768, 0.7475, and 0.8913 for the segmentation of SRD, CME, and DRT edemas, respectively.

Background: To study the visual outcomes of neovascular AMD (nAMD) treated with anti-vascular endothelial growth factor (VEGF) drugs at national level.Methods: Multicenter national database of nAMD eyes treated with anti-VEGF intravitreal injections (ranibizumab, aflibercept, bevacizumab) in fixed bimonthly (FB) or treat-and-extend (TAE) regimens. Demographics, visual acuity (VA) in logarithm of the minimum angle of resolution (logMAR) ETDRS letters at baseline and subsequent visits, number of injections and visits data were collected using a validated web-based tool (Fight Retinal Blindness!). Results: 1273 eyes (1014 patients) were included, 971 treatment naïve (TN) and 302 previously treated (PT). Baseline VA (mean ± SD) was 57.5 (±19.5) and 62.2 (±17) (p > 0.001), and 24 months final VA was 60.4 (±21.2) and 58.8 (±21.1) (p = 0.326), respectively. Mean VA change at 12/24 months was +4.2/+2.9 letters in TN eyes and +0.1/À3.4 letters in PT eyes (p < 0.001/p < 0.001). The percentage of ≥15 letters gainers/losers at 24 months was 24.8%/14.5% in TN, and 10.3%/15.7% in PT eyes. The median number of injections/visits at 12 months was 7/9 in TN and 6/8 in PT (p = 0.002/p < 0.001) and at 24 months was 11/16 in TN and 11/14 in PT (p = 0.329/p < 0.001). Study drugs included ranibizumab (39.5%), aflibercept (41.2%) and bevacizumab (19.3%).

Optical Coherence Tomography Angiography or OCTA represents one of the main means of diagnosis of Age-related Macular Degeneration (AMD), the leading cause of blindness in developed countries. This eye disease is characterized by Macular Neovascularization (MNV), the formation of vessels that tear through the retinal tissues. Four types of MNV can be distinguished, each representing different levels of severity. Both the aggressiveness of the treatment and the recovery of the patient rely on an early detection and correct diagnosis of the stage of the disease. In this work, we propose the first fully-automatic grading methodology that considers all the four clinical types of MNV at the three most relevant OCTA scanning depths for the diagnosis of AMD. We perform both a comprehensive ablation study on the contribution of said depths and an analysis of the attention maps of the network in collaboration with experts of the domain. Our proposal aims to ease the diagnosis burden and decrease the influence of subjectivity on it, offering a explainable grading through the visualization of the attention of the expert models. Our grading proposal achieved satisfactory results with an AUC of 0.9224 ± 0.0381. Additionally, the qualitative analysis performed in collaboration with experts revealed the relevance of the avascular plexus in the grading of all three types of MNV (despite not being directly involved in some of them). Thus, our proposal is not only able to robustly detect MNV in complex scenarios, but also aided to discover previously unconsidered relationships between plexuses.