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Tuesday, June 27th, 2017
Table of Contents

1 Introduction
5 PMID
 [F] Diseases Research  / PubMed Research Articles  /
Enhanced visualization of the retinal vasculature using depth information in OCT.

PubMed

 

Resource

Medical & biological engineering & computing 2017 Jun 17; ()

Authors

de Moura J1; Novo J2; Charlón P3; Barreira N4; Ortega M5;

Author Information
  • 1Department of Computer Science, University of A Coruña, 15071, A Coruña, Spain. joaquim.demoura@udc.es.
  • 2Department of Computer Science, University of A Coruña, 15071, A Coruña, Spain.
  • 3Instituto Oftalmológico Victoria de Rojas, A Coruña, Spain.
  • 4Department of Computer Science, University of A Coruña, 15071, A Coruña, Spain.
  • 5Department of Computer Science, University of A Coruña, 15071, A Coruña, Spain.

Abstract

Retinal vessel tree extraction is a crucial step for analyzing the microcirculation, a frequently needed process in the study of relevant diseases. To date, this has normally been done by using 2D image capture paradigms, offering a restricted visualization of the real layout of the retinal vasculature. In this work, we propose a new approach that automatically segments and reconstructs the 3D retinal vessel tree by combining near-infrared reflectance retinography information with Optical Coherence Tomography (OCT) sections. Our proposal identifies the vessels, estimates their calibers, and obtains the depth at all the positions of the entire vessel tree, thereby enabling the reconstruction of the 3D layout of the complete arteriovenous tree for subsequent analysis. The method was tested using 991 OCT images combined with their corresponding near-infrared reflectance retinography. The different stages of the methodology were validated using the opinion of an expert as a reference. The tests offered accurate results, showing coherent reconstructions of the 3D vasculature that can be analyzed in the diagnosis of relevant diseases affecting the retinal microcirculation, such as hypertension or diabetes, among others.



PMID

28623452

Others

Publication Type: Journal Article


This article is licensed under the the National Library of Medicine License. It uses material from the PubMed National Library of Medicine Data.


Last Modified:   2016-03-27


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