Assessment of the repeatability in an automatic methodology for hyperemia grading in the bulbar conjunctiva

When the vessels of the bulbar conjunctiva get congested with blood, a characteristic red hue appears in the area. This symptom is known as hyperemia, and can be an early indicator of certain pathologies. Therefore, a prompt diagnosis is desirable in order to minimize both medical and economic repercussions. A fully automatic methodology for hyperemia grading in the bulbar conjunctiva was developed, by means of image processing and machine learning techniques. As there is a wide range of illumination, contrast, and focus issues in the images that specialists use to perform the grading, a repeatability analysis is necessary. Thus, the validation of each step of the methodology was performed, analyzing how variations in the images are translated to the results, and comparing them to the optometrist's measurements. Our results prove the robustness of our methodology to various conditions. Moreover, the differences in the automatic outputs are similar to the optometrist's ones

Retinal OCT speckle as a biomarker for glaucoma diagnosis and staging

This paper presents a novel image analysis strategy that increases the potential of macular Optical Coherence Tomography (OCT) by using speckle features as biomarkers in different stages of glaucoma. A large pool of features (480) were computed for a subset of macular OCT volumes of the Leuven eye study cohort. The dataset contained 258 subjects that were divided into four groups based on their glaucoma severity: Healthy (56), Mild (94), Moderate (48), and Severe (60). The OCT speckle features were categorized as statistical properties, statistical distributions, contrast, spatial gray-level dependence matrices, and frequency domain features. The averaged thicknesses of ten retinal layers were also collected. Kruskal-Wallis H test and multivariable regression models were used to infer the most significant features related to glaucoma severity classification and to the correlation with visual field mean deviation. Four features were selected as being the most relevant: the ganglion cell layer (GCL) and the inner plexiform layer (IPL) thicknesses, and two OCT speckle features, the data skewness computed on the retinal nerve fiber layer (RNFL) and the scale parameter (a) of the generalized gamma distribution fitted to the GCL data. Based on a significance level of 0.05, the regression models revealed that RNFL skewness exhibited the highest significance among the features considered for glaucoma severity staging (p-values of 8.6×10-6 for the logistic model and 2.8×10-7 for the linear model). Furthermore, it demonstrated a strong negative correlation with the visual field mean deviation (ρ=-0.64). The post hoc analysis revealed that, when distinguishing healthy controls from glaucoma subjects, GCL thickness is the most relevant feature (p-value of 8.7×10-5). Conversely, when comparing the Mild versus Moderate stages of glaucoma, RNFL skewness emerged as the only feature exhibiting statistical significance (p-value = 0.001). This work shows that macular OCT speckle contains information that is currently not used in clinical practice, and not only complements structural measurements (thickness) but also has a potential for glaucoma staging

CIBERER : Spanish national network for research on rare diseases: A highly productive collaborative initiative

Altres ajuts: Instituto de Salud Carlos III (ISCIII); Ministerio de Ciencia e Innovación.CIBER (Center for Biomedical Network Research; Centro de Investigación Biomédica En Red) is a public national consortium created in 2006 under the umbrella of the Spanish National Institute of Health Carlos III (ISCIII). This innovative research structure comprises 11 different specific areas dedicated to the main public health priorities in the National Health System. CIBERER, the thematic area of CIBER focused on rare diseases (RDs) currently consists of 75 research groups belonging to universities, research centers, and hospitals of the entire country. CIBERER's mission is to be a center prioritizing and favoring collaboration and cooperation between biomedical and clinical research groups, with special emphasis on the aspects of genetic, molecular, biochemical, and cellular research of RDs. This research is the basis for providing new tools for the diagnosis and therapy of low-prevalence diseases, in line with the International Rare Diseases Research Consortium (IRDiRC) objectives, thus favoring translational research between the scientific environment of the laboratory and the clinical setting of health centers. In this article, we intend to review CIBERER's 15-year journey and summarize the main results obtained in terms of internationalization, scientific production, contributions toward the discovery of new therapies and novel genes associated to diseases, cooperation with patients' associations and many other topics related to RD research

Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging

status: publishe

Microvascular damage assessed by OCT Angiography for glaucoma diagnosis: a systematic review of the most discriminative regions (vol 45, pg 913, 2020)

status: publishe

OCTA Multilayer and Multisector Peripapillary Microvascular Modeling for Diagnosing and Staging of Glaucoma

Purpose: To develop and assess an automatic procedure for classifying and staging glaucomatous vascular damage based on optical coherence tomography angiography (OCTA) imaging. Methods: OCTA scans (Zeiss Cirrus 5000 HD-OCT) from a random eye of 39 healthy subjects and 82 glaucoma patients were used to develop a new classification algorithm based on multilayer and multisector information. The averaged circumpapillary retinal nerve fiber layer (RNFL) thickness was also collected. Three models, support vector machine (SVM), random forest (RF), and gradient boosting (xGB), were developed and optimized for classifying between healthy and glaucoma patients, primary open-angle glaucoma (POAG) and normal-tension glaucoma (NTG), and glaucoma severity groups. Results: All the models, the SVM (area under the receiver operating characteristic [AUROC] 0.89 ± 0.06), the RF (AUROC 0.86 ± 0.06), and the xGB (AUROC 0.85 ± 0.07), with 26, 22, and 29 vascular features obtained after feature selection, respectively, presented a similar performance to the RNFL thickness (AUROC 0.85 ± 0.06) in classifying healthy and glaucoma patients. The superficial vascular plexus was the most informative layer with the infero temporal sector as the most discriminative region of interest. No significant differentiation was obtained in discriminating the POAG from the NTG group. The xGB model, after feature selection, presented the best performance in classifying the severity groups (AUROC 0.76 ± 0.06), outperforming the RNFL (AUROC 0.67 ± 0.06). Conclusions: OCTA multilayer and multisector information has similar performance to RNFL for glaucoma diagnosis, but it has an added value for glaucoma severity classification, showing promising results for staging glaucoma progression. Translational Relevance: OCTA, in its current stage, has the potential to be used in clinical practice as a complementary imaging technique in glaucoma management.status: publishe

Hard Exudates Segmentation in Fundus Image via Combining Automatic and CNN-based Interactive Methods

Hard exudates (EX) are one of the early signs of the diabetic retinopathy (DR), a leading cause of irreversible blindness. Computer assisted segmentation method makes screening faster and provide quantitative measurements of EX lesions. In this paper, we propose a method which takes advantage of both an automatic segmentation model and an interactive one which enables click-based corrections in the case of supoptimal EX segmentation. We evaluated the proposed method on three fundus image datasets including two public datasets (IDRiD and DDR) and a private dataset (E-Hos) using the area under precision-recall curve (AUPR) metric. The results show that the proposed method achieved AUPR scores of 0.851, 0.693 and 0.761 on the IDRiD, DDR and E-Hos datasets, respectively. The experiments also demonstrates that the proposed method outperforms several state-of-the-art CNNs in EX lesion segmentation