Automated segmentation of microtomography imaging of Egyptian mummies
This work addresses the need for efficient segmentation in archaeological analysis, though it is incremental as it builds on existing machine learning methods for a specific domain.
The researchers tackled the labor-intensive segmentation of microtomography images from Egyptian animal mummies by developing an automated tool, achieving 94-98% accuracy compared to manual segmentation, which is close to commercial deep learning software at lower complexity.
Propagation Phase Contrast Synchrotron Microtomography (PPC-SR$μ$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.