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Automated segmentation of microtomography imaging of Egyptian mummies.
- Source :
-
PloS one [PLoS One] 2021 Dec 15; Vol. 16 (12), pp. e0260707. Date of Electronic Publication: 2021 Dec 15 (Print Publication: 2021). - Publication Year :
- 2021
-
Abstract
- 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% 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.<br />Competing Interests: The authors have declared that no competing interests exist.
Details
- Language :
- English
- ISSN :
- 1932-6203
- Volume :
- 16
- Issue :
- 12
- Database :
- MEDLINE
- Journal :
- PloS one
- Publication Type :
- Academic Journal
- Accession number :
- 34910736
- Full Text :
- https://doi.org/10.1371/journal.pone.0260707