Automated segmentation of microtomography imaging of Egyptian mummies
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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 manually segmented slices, approaching the results of off-the-shelf commercial software using deep learning (97-99 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.
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