Automated segmentation of microtomography imaging of Egyptian mummies

PLoS One. 2021 Dec 15;16(12):e0260707. doi: 10.1371/journal.pone.0260707. eCollection 2021.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Automation
  • Egypt
  • Humans
  • Machine Learning
  • Mummies / diagnostic imaging*
  • X-Ray Microtomography / methods*

Grants and funding

The Automated SEgmentation of Microtomography Imaging (ASEMI) project has received funding from the ATTRACT project funded by the EC under Grant Agreement 777222. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.