Automated segmentation of microtomography imaging of Egyptian mummies.
PLoS One
; 16(12): e0260707, 2021.
Article
em En
| MEDLINE
| ID: mdl-34910736
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Múmias
/
Microtomografia por Raio-X
Tipo de estudo:
Qualitative_research
Limite:
Humans
País/Região como assunto:
Africa
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article