Dataset Information

Network-based features for retinal fundus vessel structure analysis.

ABSTRACT: Retinal fundus imaging is a non-invasive method that allows visualizing the structure of the blood vessels in the retina whose features may indicate the presence of diseases such as diabetic retinopathy (DR) and glaucoma. Here we present a novel method to analyze and quantify changes in the retinal blood vessel structure in patients diagnosed with glaucoma or with DR. First, we use an automatic unsupervised segmentation algorithm to extract a tree-like graph from the retina blood vessel structure. The nodes of the graph represent branching (bifurcation) points and endpoints, while the links represent vessel segments that connect the nodes. Then, we quantify structural differences between the graphs extracted from the groups of healthy and non-healthy patients. We also use fractal analysis to characterize the extracted graphs. Applying these techniques to three retina fundus image databases we find significant differences between the healthy and non-healthy groups (p-values lower than 0.005 or 0.001 depending on the method and on the database). The results are sensitive to the segmentation method (manual or automatic) and to the resolution of the images.

SUBMITTER: Amil P

PROVIDER: S-EPMC6658152 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Network-based features for retinal fundus vessel structure analysis.

Amil Pablo P Reyes-Manzano Cesar F CF Guzmán-Vargas Lev L Sendiña-Nadal Irene I Masoller Cristina C

PloS one 20190725 7

Retinal fundus imaging is a non-invasive method that allows visualizing the structure of the blood vessels in the retina whose features may indicate the presence of diseases such as diabetic retinopathy (DR) and glaucoma. Here we present a novel method to analyze and quantify changes in the retinal blood vessel structure in patients diagnosed with glaucoma or with DR. First, we use an automatic unsupervised segmentation algorithm to extract a tree-like graph from the retina blood vessel structur ...[more]

PMID: 31344132

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Project description:BackgroundChanges in the retinal vessel caliber are associated with a variety of major diseases, namely diabetes, hypertension and atherosclerosis. The clinical assessment of these changes in fundus images is tiresome and prone to errors and thus automatic methods are desirable for objective and precise caliber measurement. However, the variability of blood vessel appearance, image quality and resolution make the development of these tools a non-trivial task.MetholodogyA method for the estimation of vessel caliber in eye fundus images via vessel cross-sectional intensity profile model fitting is herein proposed. First, the vessel centerlines are determined and individual segments are extracted and smoothed by spline approximation. Then, the corresponding cross-sectional intensity profiles are determined, post-processed and ultimately fitted by newly proposed parametric models. These models are based on Difference-of-Gaussians (DoG) curves modified through a multiplying line with varying inclination. With this, the proposed models can describe profile asymmetry, allowing a good adjustment to the most difficult profiles, namely those showing central light reflex. Finally, the parameters of the best-fit model are used to determine the vessel width using ensembles of bagged regression trees with random feature selection.Results and conclusionsThe performance of our approach is evaluated on the REVIEW public dataset by comparing the vessel cross-sectional profile fitting of the proposed modified DoG models with 7 and 8 parameters against a Hermite model with 6 parameters. Results on different goodness of fitness metrics indicate that our models are constantly better at fitting the vessel profiles. Furthermore, our width measurement algorithm achieves a precision close to the observers, outperforming state-of-the art methods, and retrieving the highest precision when evaluated using cross-validation. This high performance supports the robustness of the algorithm and validates its use in retinal vessel width measurement and possible integration in a system for retinal vasculature assessment.

Project description:PurposeOCT has revealed many details of retinal disease that were not available with older imaging technologies. In eyes of adults older than 60 years with healthy maculas as determined by color fundus photography (CFP) and a validated grading system, we screened for pathologic features using OCT. We also tested visual function to assess potential impact of the observed pathologic features on patients.DesignCross-sectional study.ParticipantsPersons recruited from primary ophthalmology care clinics.MethodsColor fundus photographs were assessed by the 9-step Age-Related Eye Disease Study scale. OCT macular volumes of participants at step 1 on the Age-Related Eye Disease Study scale, considered healthy, were reviewed by a retina specialist masked to other participant characteristics. Participants were tested for 6 different cone- and rod-mediated visual functions.Main outcome measuresPercentage of participants with disorders detected on OCT review and visual function measures.ResultsIn 138 of 984 eyes (14%) considered healthy by CFP, pathologic features were detectable by OCT, with 8.4% having vitreomacular interface disorders. Among the low-prevalence disorders found, 5 eyes (0.5%) showed macular telangiectasia type 2. Relative to eyes lacking detectable chorioretinal pathologic features, eyes with any pathologic features were associated with poorer low-luminance visual acuity and rod-mediated dark adaptation. In eyes with epiretinal membranes, the largest single entity identified (n = 61 [6.2%]), significantly worse visual functions were best-corrected visual acuity (P = 0.0444), low-luminance visual acuity (P = 0.0151), and light sensitivity (central 3° and 9°; P = 0.0035 and P = 0.0097, respectively).ConclusionsMacular pathologic features with functional visual implications not identified by clinical examination or CFP are detectable with OCT. Vitreomacular interface disorders often are visually significant and treatable conditions that are visible on OCT, but are easily missed on CFP and clinical examination. Another such condition best seen on OCT is macular telangiectasia type 2, an untreatable disorder for which a clinical trial is in progress. OCT has a potential role in primary eye care clinics to screen for retinal pathologic features, especially in eyes with decreased visual acuity and otherwise normal examination results.

Dataset Information

Network-based features for retinal fundus vessel structure analysis.

Publications

Network-based features for retinal fundus vessel structure analysis.

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