  Propagation Phase Contrast Synchrotron Microtomography (PPC-SR${\mu}$CT) 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.
