Evidence map›Paper›PMID 38100790›Full record

ArticleBiomedical physics & engineering express2024

Evaluation of manual and automated approaches for segmentation and extraction of quantitative indices from [

Georgios Krokos, Tejas Kotwal, Afnan Malaih, Sally Barrington, Price Jackson, Rodney J Hicks, Paul K Marsden, Barbara Malene Fischer

Open access · hybridAbstract read
In one paragraph

Article in Biomedical physics & engineering express, 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
1.1field-weighted citation impact, top 20% 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

0 citing papers in PubMed, 5 citations in OpenAlex.

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

8 authors at 4 institutions in 3 countries.

Georgios KrokosSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.ORCID 0000-0002-9776-4714
Tejas KotwalSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Afnan MalaihSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Sally BarringtonSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Price JacksonPeter MacCallum Cancer Centre, Melbourne, Australia.
Rodney J HicksDepartment of Medicine, St Vincent's Hospital Medical School, the University of Melbourne, Australia.
Paul K MarsdenSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Barbara Malene FischerSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
King's College London · GBPeter MacCallum Cancer Centre · AUThe University of Melbourne · AUUniversity of Copenhagen · DK

Funding

Wellcome Trust
6 · The paper itself

Abstract

Utilisation of whole organ volumes to extract anatomical and functional information from computed tomography (CT) and positron emission tomography (PET) images may provide key information for the treatment and follow-up of cancer patients. However, manual organ segmentation, is laborious and time-consuming. In this study, a CT-based deep learning method and a multi-atlas method were evaluated for segmenting the liver and spleen on CT images to extract quantitative tracer information from Fluorine-18 fluorodeoxyglucose ([

Indexed as

Fluorodeoxyglucose F18Positron Emission Tomography Computed TomographyHumansLiverPositron-Emission TomographyTomography, X-Ray ComputedFluorodeoxyglucose F18atlasCTdeep learningorgan segmentationPET

Identifiers

PMID38100790
PMCPMC10767880
OpenAlexW4389815195

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.