Marc Tanti (1,4), Camille Berruyer (2), Paul Tafforeau (2), Adrian Muscat (1), Reuben Farrugia (1), Kenneth Scerri (3), Gianluca Valentino (1), V. Armando Solé (2), Johann A. Briffa (1)
arXiv, May 2021. DOI: arXiv:2105.06738

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 term of usability to those from deep learning, justifying the use of these techniques.

How Our Software Was Used

Dragonfly was used image segmentation. It was also used to create a 5-level U-Net model.

Author Affiliation

(1) Dept. of Comm. & Computer Engineering, University of Malta, Msida MSD 2080, Malta.
(2) European Synchrotron Radiation Facility, 71 Avenue des Martyrs, CS-40220, 38043 Grenoble cedex 09, France.
(3) Dept. of Systems & Control Engineering, University of Malta, Msida MSD 2080, Malta.
(4) Institute of Linguistics & Language Technology, University of Malta, Msida MSD 2080, Malta.