Evidence map›Paper›PMID 38826408›Full record

ArticlebioRxiv : the preprint server for biology2024

VesselBoost: A Python Toolbox for Small Blood Vessel Segmentation in Human Magnetic Resonance Angiography Data.

Marshall Xu, Fernanda L Ribeiro, Markus Barth, Michaël Bernier, Steffen Bollmann, Soumick Chatterjee, Francesco Cognolato, Omer Faruk Gulban, Vaibhavi Itkyal, Siyu Liu and 5 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Marshall XuSchool of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, QLD, Australia.ORCID 0009-0007-1280-7361
Fernanda L RibeiroSchool of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, QLD, Australia.
Markus BarthSchool of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, QLD, Australia.ORCID 0000-0002-0520-1843
Michaël BernierAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
Steffen BollmannSchool of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, QLD, Australia.
Soumick ChatterjeeDepartment of Biomedical Magnetic Resonance, Institute of Experimental Physics, Otto-von-Guericke-University, Magdeburg, ST, Germany.
Francesco CognolatoCentre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia.
Omer Faruk GulbanFaculty of Psychology and Neuroscience, Maastricht University, Maastricht, LI, Netherlands.ORCID 0000-0001-7761-3727
Vaibhavi ItkyalDepartment of Biotechnology, Indian Institute of Technology, Madras, TN, India.
Siyu LiuSchool of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, QLD, Australia.
Hendrik MatternDepartment of Biomedical Magnetic Resonance, Institute of Experimental Physics, Otto-von-Guericke-University, Magdeburg, ST, Germany.
Jonathan R PolimeniAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
Thomas B ShawSchool of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, QLD, Australia.ORCID 0000-0003-2490-0532
Oliver SpeckDepartment of Biomedical Magnetic Resonance, Institute of Experimental Physics, Otto-von-Guericke-University, Magdeburg, ST, Germany.
Saskia BollmannSchool of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, QLD, Australia.

Funding

Project 4U19NS123717 · NINDS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI LINNINGER, ANDREAS A · 2021 to 2025
$13.4M
Improving Human fMRI through Modeling and Imaging Microvascular Dynamics: Administrative SupplementR01MH111419 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI POLIMENI, JONATHAN RIZZO · 2016 to 2020
$4.9M
NIMH NIH HHS R01 MH111419NINDS NIH HHS U19 NS123717
6 · The paper itself

Abstract

Magnetic resonance angiography (MRA) performed at ultra-high magnetic field provides a unique opportunity to study the arteries of the living human brain at the mesoscopic level. From this, we can gain new insights into the brain's blood supply and vascular disease affecting small vessels. However, for quantitative characterization and precise representation of human angioarchitecture to, for example, inform blood-flow simulations, detailed segmentations of the smallest vessels are required. Given the success of deep learning-based methods in many segmentation tasks, we here explore their application to high-resolution MRA data, and address the difficulty of obtaining large data sets of correctly and comprehensively labelled data. We introduce

Identifiers

PMID38826408
PMCPMC11142164

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.