Evidence map›Paper›PMID 38834987›Full record

ArticleBMC medical imaging2024

3D Tortuosity computation as a shape descriptor and its application to brain structure analysis.

Maria-Julieta Mateos, Ernesto Bribiesca, Adolfo Guzmán-Arenas, Wendy Aguilar, Jorge A Marquez-Flores

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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.

Maria-Julieta MateosGraduate Program in Computer Science and Engineering, Universidad Nacional Autónoma de México, Mexico City, México. julieta8a3@gmail.com.
Ernesto BribiescaInstitute of Research in Applied Mathematics and Systems (IIMAS), Universidad Nacional Autónoma de México, Circuito Escolar 3000, Ciudad Universitaria, 04510, Coyoacán, Mexico City, México.
Adolfo Guzmán-ArenasCentro de Investigación en Computación, Instituto Politécnico Nacional, Mexico City, México.
Wendy AguilarInstitute of Research in Applied Mathematics and Systems (IIMAS), Universidad Nacional Autónoma de México, Circuito Escolar 3000, Ciudad Universitaria, 04510, Coyoacán, Mexico City, México.
Jorge A Marquez-FloresInstituto de Ciencias Aplicadas y Tecnología, Universidad Nacional Autónoma de México, Universidad Nacional Autónoma de México, Circuito Exterior S/N, Ciudad Universitaria, 04510, Mexico City, México.

Funding

Consejo Nacional de Ciencia y Tecnología 48059
6 · The paper itself

Abstract

In this study, we propose a novel method for quantifying tortuosity in 3D voxelized objects. As a shape characteristic, tortuosity has been widely recognized as a valuable feature in image analysis, particularly in the field of medical imaging. Our proposed method extends the two-dimensional approach of the Slope Chain Code (SCC) which creates a one-dimensional representation of curves. The utility of 3D tortuosity (

Indexed as

Alzheimer DiseaseBrainImaging, Three-DimensionalAgedAlgorithmsCase-Control StudiesFemaleHumansMagnetic Resonance ImagingMale3D tortuosityAlzheimer’s diseaseBrain morphologyDiscrete tortuosity

Identifiers

PMID38834987
PMCPMC11149256

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.