March 2012
Volume 53, Issue 14
Free
ARVO Annual Meeting Abstract  |   March 2012
3D Retinal Vascular Network from OCT data
Author Affiliations & Notes
  • Pedro M. Rodrigues
    CNTM, Association for Innovation and Biomedical Research on Light and Image, Coimbra, Portugal
  • Pedro Guimarães
    Institute of Biomedical Research in Light and Image, Faculty of Medicine, University of Coimbra, Coimbra, Portugal
  • Pedro Serranho
    Institute of Biomedical Research in Light and Image, Faculty of Medicine, University of Coimbra, Coimbra, Portugal
    Mathematics Section, Department of Science and Technology, Open University, Lisbon, Portugal
  • Rui Bernardes
    CNTM, Association for Innovation and Biomedical Research on Light and Image, Coimbra, Portugal
    Institute of Biomedical Research in Light and Image, Faculty of Medicine, University of Coimbra, Coimbra, Portugal
  • Footnotes
    Commercial Relationships  Pedro M. Rodrigues, None; Pedro Guimarães, None; Pedro Serranho, None; Rui Bernardes, None
  • Footnotes
    Support  Fundação para a Ciência e a Tecnologia under the research project PTDC/SAU-ENB/111139/2009 and program COMPETE (FCOMP-01-0124-FEDER-015712).
Investigative Ophthalmology & Visual Science March 2012, Vol.53, 4099. doi:
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      Pedro M. Rodrigues, Pedro Guimarães, Pedro Serranho, Rui Bernardes; 3D Retinal Vascular Network from OCT data. Invest. Ophthalmol. Vis. Sci. 2012;53(14):4099.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose: : To compute the 3D vascular network of the human macula from standard high-definition optical coherence tomography (HD-OCT) data.

Methods: : Scans from the macular region of the human eye were obtained by HD-OCT (Cirrus HD-OCT, Carl Zeiss Meditec, Dublin, CA, USA). Data volumes of 512x128x1024 and 200x200x1024 voxels were gathered from the central 6000x6000x2000 cubic micrometers of the ocular fundus from each of the 10 eyes of 10 healthy volunteers and 6 eyes from 5 patients with type 2 diabetes. OCT reflectivity data was exported using the Cirrus Review Software to be processed and analyzed. Additionally, the surfaces defining the inner limiting membrane and the top of the retinal pigment epithelium (RPE) were exported allowing to flatten the RPE and so to co-register A-scans depth-wise. We resort to a proprietary technique for the segmentation of the retinal vascular network from OCT fundus reference images. It thus becomes possible to classify each A-scan into the vessel and non-vessel A-scan classes, respectively the ones within the vessel and non-vessel regions. To locate vessels depth-wise within the retina, we resort to particular characteristics identifying the presence of a vessel, namely the hyper-reflectivity characteristic of the top of the vessel and the shadowing effect due to the light absorption by the blood. In this way, we locally compared A-scans classified as the vessel class with A-scans classified as the non-vessel class to highlight the differences due to the presence of vessels.

Results: : The methodology proposed allows for the determination of both the hyper-reflectivity region and the shadowing effect, and therefore to determine the location of vessels depth-wise within the human retina. In addition, the results achieved show to be coherent as demonstrated by the smoothness of vessels across different B-scans and the possibility to discriminate between different depths at vessel crossovers.

Conclusions: : The findings suggest the possibility to compute the vascular network in 3-dimensions from the human macula, non-invasively, using a standard high-definition OCT.

Clinical Trial: : http://www.clinicaltrials.gov NCT01220804

Keywords: imaging/image analysis: clinical • image processing • retina 
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