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.
@article{arxiv.2105.06738,
title = {Automated segmentation of microtomography imaging of Egyptian mummies},
author = {Marc Tanti and Camille Berruyer and Paul Tafforeau and Adrian Muscat and Reuben Farrugia and Kenneth Scerri and Gianluca Valentino and V. Armando Solé and Johann A. Briffa},
journal= {arXiv preprint arXiv:2105.06738},
year = {2021}
}