Automated segmentation of microtomography imaging of Egyptian mummies

TL;DR

This paper presents an automated machine learning-based tool for segmenting microtomography images of Egyptian mummies, achieving high accuracy and reducing manual effort compared to traditional methods.

Contribution

The authors developed a new automated segmentation tool for microtomography images that performs comparably to deep learning methods but with lower complexity.

Findings

Achieved 94-98% accuracy in segmentation

Close in usability to deep learning approaches

Reduced manual effort in segmentation process

Abstract

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.