Evidence map›Paper›PMID 34838018›Full record

ReviewBiomedical engineering online2021

Automated landmarking of bends in vascular structures: a comparative study with application to the internal carotid artery.

Henrik A Kjeldsberg, Aslak W Bergersen, Kristian Valen-Sendstad

Open access · goldAbstract readReview
In one paragraph

Review in Biomedical engineering online, 2021. 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
–field-weighted citation impact, top 74% 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, 1 citations in OpenAlex.

  1. Multivariate Normal Distribution Method for a Virtual Cerebral Arterial Population.International journal for numerical methods in biomedical engineering · 2025
    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

3 authors at 1 institution in 1 country.

Henrik A KjeldsbergDepartment of Computational Physiology, Simula Research Laboratory AS, Kristian Augusts gate 23, 0164, Oslo, Norway.
Aslak W BergersenDepartment of Computational Physiology, Simula Research Laboratory AS, Kristian Augusts gate 23, 0164, Oslo, Norway.
Kristian Valen-SendstadDepartment of Computational Physiology, Simula Research Laboratory AS, Kristian Augusts gate 23, 0164, Oslo, Norway. kvs@simula.no.ORCID http://orcid.org/0000-0002-2907-0171
Simula Research Laboratory · NO

Funding

National Research Council of Science and Technology 262827
6 · The paper itself

Abstract

Automated tools for landmarking the internal carotid artery (ICA) bends have the potential for efficient and objective medical image-based morphometric analysis. The two existing algorithms rely on numerical approximations of curvature and torsion of the centerline. However, input parameters, original source code, comparability, and robustness of the algorithms remain unknown. To address the former two, we have re-implemented the algorithms, followed by sensitivity analyses. Of the input parameters, the centerline smoothing had the least impact resulting in 6-7 bends, which is anatomically realistic. In contrast, centerline resolution showed to completely over- and underestimated the number of bends varying from 3 to 33. Applying the algorithms to the same cohort revealed a variability that makes comparison of results between previous studies questionable. Assessment of robustness revealed how one algorithm is vulnerable to model smoothness and noise, but conceptually independent of application. In contrast, the other algorithm is robust and consistent, but with limited general applicability. In conclusion, both algorithms are equally valid albeit they produce vastly different results. We have provided a well-documented open-source implementation of the algorithms. Finally, we have successfully performed this study on the ICA, but application to other vascular regions should be performed with caution.

Indexed as

Carotid Artery, InternalImaging, Three-DimensionalAlgorithmsHumansAutomated landmarkingComputational geometryGeometric characterizationGeometric risk factorInternal carotid arteryLandmark detection

Identifiers

PMID34838018
PMCPMC8626959
OpenAlexW3217356068

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.