Automated segmentation of microtomography imaging of Egyptian mummies

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.

References in corpus (5)

• Scikit-learn: Machine Learning in Python
• PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
• CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation
• PyHST2: an hybrid distributed code for high speed tomographic reconstruction with iterative reconstruction and a priori knowledge capabilities
• Automated segmentation of microtomography imaging of Egyptian mummies

Cited by in corpus (1)

• Automated segmentation of microtomography imaging of Egyptian mummies