Evidence map›Paper›PMID 37089699›Full record

ArticleMolecular vision2022

Novel segmentation algorithm for high-throughput analysis of spectral domain-optical coherence tomography imaging of teleost retinas.

Kent R Barter, Hélène Paradis, Robert L Gendron, Josué A Lily Vidal, Oscar Meruvia-Pastor

Open access · greenAbstract read
In one paragraph

Article in Molecular vision, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 33% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Kent R BarterDepartment of Computer Science, Memorial University, St. John's, A1B 3X5, NL, Canada.
Hélène ParadisFaculty of Medicine, Memorial University, St. John's, A1B 3V6, NL, Canada.
Robert L GendronFaculty of Medicine, Memorial University, St. John's, A1B 3V6, NL, Canada.
Josué A Lily VidalDepartment of Ophthalmology, College of Medicine, University of Saskatchewan, Saskatoon, SK, S7K 0M7, Canada.
Oscar Meruvia-PastorDepartment of Computer Science, Memorial University, St. John's, A1B 3X5, NL, Canada.
Memorial University of Newfoundland · CAUniversity of Saskatchewan · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Spectral domain-optical coherence tomography (SD-OCT) has become an essential tool for assessing ocular tissues in live subjects and conducting research on ocular development, health, and disease. The processing of SD-OCT images, particularly those from non-mammalian species, is a labor-intensive manual process due to a lack of automated analytical programs. This paper describes the development and implementation of a novel computer algorithm for the quantitative analysis of SD-OCT images of live teleost eyes. Automated segmentation processing of SD-OCT images of retinal layers was developed using a novel algorithm based on thresholding. The algorithm measures retinal thickness characteristics in a large volume of imaging data of teleost ocular structures in a short time, providing increased accuracy and repeatability of SD-OCT image analysis over manual measurements. The algorithm also generates hundreds of retinal thickness measurements per image for a large number of images for a given dataset. Meanwhile, heat mapping software that plots SD-OCT image measurements as a color gradient was also created. This software directly converts the measurements of each processed image to represent changes in thickness across the whole retinal scan. It also enables 2D and 3D visualization of retinal thickness across the scan, facilitating specimen comparison and localization of areas of interest. The study findings showed that the novel algorithm is more accurate, reliable, and repeatable than manual SD-OCT analysis. The adaptability of the algorithm makes it potentially suitable for analyzing SD-OCT scans of other non-mammalian species.

Indexed as

RetinaTomography, Optical CoherenceAlgorithmsHumansImage Processing, Computer-AssistedSoftware

Identifiers

PMID37089699
PMCPMC10115363
OpenAlexW4366822497

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.