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Tanti M, Berruyer C, Tafforeau P, et al. Automated segmentation of microtomography imaging of Egyptian mummies. PLoS One. 2021;16(12):e0260707doi: 10.1371/journal.pone.0260707.
Tanti, M., Berruyer, C., Tafforeau, P., Muscat, A., Farrugia, R., Scerri, K., Valentino, G., Solé, V. A., & Briffa, J. A. (2021). Automated segmentation of microtomography imaging of Egyptian mummies. PloS one, 16(12), e0260707. https://doi.org/10.1371/journal.pone.0260707
Tanti, Marc, et al. "Automated segmentation of microtomography imaging of Egyptian mummies." PloS one vol. 16,12 (2021): e0260707. doi: https://doi.org/10.1371/journal.pone.0260707
Tanti M, Berruyer C, Tafforeau P, Muscat A, Farrugia R, Scerri K, Valentino G, Solé VA, Briffa JA. Automated segmentation of microtomography imaging of Egyptian mummies. PLoS One. 2021 Dec 15;16(12):e0260707. doi: 10.1371/journal.pone.0260707. eCollection 2021. PMID: 34910736; PMCID: PMC8673632.
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PLoS One. 2021 Dec 15;16(12):e0260707. doi: 10.1371/journal.pone.0260707. eCollection 2021.

Automated segmentation of microtomography imaging of Egyptian mummies.

PloS one

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

Affiliations

  1. Dept. of Comm. & Computer Engineering, University of Malta, Msida, Malta.
  2. European Synchrotron Radiation Facility, Grenoble, France.
  3. Dept. of Systems & Control Engineering, University of Malta, Msida, Malta.

PMID: 34910736 PMCID: PMC8673632 DOI: 10.1371/journal.pone.0260707

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.

Conflict of interest statement

The authors have declared that no competing interests exist.

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