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Enhanced visualization of the retinal vasculature using depth information in OCT

Overview of attention for article published in Medical & Biological Engineering & Computing, June 2017
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Mentioned by

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2 tweeters

Citations

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12 Dimensions

Readers on

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9 Mendeley
Title
Enhanced visualization of the retinal vasculature using depth information in OCT
Published in
Medical & Biological Engineering & Computing, June 2017
DOI 10.1007/s11517-017-1660-8
Pubmed ID
Authors

Joaquim de Moura, Jorge Novo, Pablo Charlón, Noelia Barreira, Marcos Ortega

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.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 2 22%
Researcher 2 22%
Student > Doctoral Student 1 11%
Student > Ph. D. Student 1 11%
Student > Master 1 11%
Other 2 22%
Readers by discipline Count As %
Engineering 5 56%
Computer Science 2 22%
Unspecified 2 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 October 2017.
All research outputs
#7,485,497
of 12,022,940 outputs
Outputs from Medical & Biological Engineering & Computing
#1,125
of 1,321 outputs
Outputs of similar age
#151,270
of 268,980 outputs
Outputs of similar age from Medical & Biological Engineering & Computing
#3
of 10 outputs
Altmetric has tracked 12,022,940 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,321 research outputs from this source. They receive a mean Attention Score of 3.8. This one is in the 14th percentile – i.e., 14% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 268,980 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 7 of them.