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 and Johann A Briffa

PLOS ONE, 2021, vol. 16, issue 12, 1-26

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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0260707

DOI: 10.1371/journal.pone.0260707

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