Evidence map›Paper›PMID 34910736›Full record

ArticlePloS one2021

Automated segmentation of microtomography imaging of Egyptian mummies.

Marc Tanti, Camille Berruyer, Paul Tafforeau, Adrian Muscat, Reuben Farrugia, Kenneth Scerri, Gianluca Valentino, V Armando Solé, Johann A Briffa

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 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
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. 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

9 authors.

Marc TantiDept. of Comm. & Computer Engineering, University of Malta, Msida, Malta.
Camille BerruyerEuropean Synchrotron Radiation Facility, Grenoble, France.ORCID 0000-0002-0046-2972
Paul TafforeauEuropean Synchrotron Radiation Facility, Grenoble, France.ORCID 0000-0002-5962-1683
Adrian MuscatDept. of Comm. & Computer Engineering, University of Malta, Msida, Malta.
Reuben FarrugiaDept. of Comm. & Computer Engineering, University of Malta, Msida, Malta.
Kenneth ScerriDept. of Systems & Control Engineering, University of Malta, Msida, Malta.
Gianluca ValentinoDept. of Comm. & Computer Engineering, University of Malta, Msida, Malta.ORCID 0000-0003-3864-7785
V Armando SoléEuropean Synchrotron Radiation Facility, Grenoble, France.
Johann A BriffaDept. of Comm. & Computer Engineering, University of Malta, Msida, Malta.ORCID 0000-0003-3532-9474

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Propagation Phase Contrast Synchrotron Microtomography (PPC-SRμCT) is the gold standard for non-invasive and non-destructive access to internal structures of archaeological remains. In this analysis, the virtual specimen needs to be segmented to separate different parts or materials, a process that normally requires considerable human effort. In the Automated SEgmentation of Microtomography Imaging (ASEMI) project, we developed a tool to automatically segment these volumetric images, using manually segmented samples to tune and train a machine learning model. For a set of four specimens of ancient Egyptian animal mummies we achieve an overall accuracy of 94-98% when compared with manually segmented slices, approaching the results of off-the-shelf commercial software using deep learning (97-99%) at much lower complexity. A qualitative analysis of the segmented output shows that our results are close in terms of usability to those from deep learning, justifying the use of these techniques.

Indexed as

AlgorithmsAutomationEgyptHumansMachine LearningMummiesX-Ray Microtomography

Identifiers

PMID34910736
PMCPMC8673632

What OpenQuestion holds

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LicenceCC BY
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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.