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Quantitative analysis of retinal OCT

Overview of attention for article published in Medical Image Analysis, July 2016
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Article details
Title
Quantitative analysis of retinal OCT
Published in
Medical Image Analysis, July 2016
DOI 10.1016/j.media.2016.06.001
Pubmed ID
Authors
Abstract

Clinical acceptance of 3-D OCT retinal imaging brought rapid development of quantitative 3-D analysis of retinal layers, vasculature, retinal lesions as well as facilitated new research in retinal diseases. One of the cornerstones of many such analyses is segmentation and thickness quantification of retinal layers and the choroid, with an inherently 3-D simultaneous multi-layer LOGISMOS (Layered Optimal Graph Image Segmentation for Multiple Objects and Surfaces) segmentation approach being extremely well suited for the task. Once retinal layers are segmented, regional thickness, brightness, or texture-based indices of individual layers can be easily determined and thus contribute to our understanding of retinal or optic nerve head (ONH) disease processes and can be employed for determination of disease status, treatment responses, visual function, etc. Out of many applications, examples provided in this paper focus on image-guided therapy and outcome prediction in age-related macular degeneration and on assessing visual function from retinal layer structure in glaucoma.

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X Demographics

X Demographics

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

Mendeley demographics

The data shown below were compiled from readership statistics for 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 1 1%
United Kingdom 1 1%
Unknown 85 98%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 19 22%
Researcher 12 14%
Student > Doctoral Student 9 10%
Student > Master 8 9%
Other 3 3%
Other 11 13%
Unknown 25 29%
Readers by discipline
Readers by discipline Count As %
Computer Science 17 20%
Medicine and Dentistry 14 16%
Engineering 10 11%
Neuroscience 7 8%
Unspecified 2 2%
Other 9 10%
Unknown 28 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 10 August 2016.
All research outputs
#19,944,994
of 25,374,647 outputs
Outputs from Medical Image Analysis
#1,333
of 1,653 outputs
Outputs of similar age
#272,318
of 370,093 outputs
Outputs of similar age from Medical Image Analysis
#27
of 39 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,653 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one is in the 17th percentile – i.e., 17% 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 370,093 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 39 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.