Confirmatory Results
A machine learning based approach to the segmentation of micro CT data in archaeological and evolutionary sciences
View ORCID ProfileThomas O’Mahoney, View ORCID ProfileLidija Mcknight, Tristan Lowe, Maria Mednikova, View ORCID ProfileJacob Dunn
doi: https://doi.org/10.1101/859983
Thomas O’Mahoney
1School of Life Sciences, Faculty of Science and Engineering, Anglia Ruskin University, Cambridge, UK
2McDonald institute for Archaeological Research, University of Cambridge, Cambridge, UK
Lidija Mcknight
3Interdisciplinary Centre for Ancient Life, University of Manchester, Manchester, UK
Tristan Lowe
3Interdisciplinary Centre for Ancient Life, University of Manchester, Manchester, UK
4Henry Mosely X-Ray Imaging Facility, University of Manchester, Manchester, UK
Maria Mednikova
5Institute of Archaeology, Russian Academy of Sciences, Moscow, Russian Federation
Jacob Dunn
1School of Life Sciences, Faculty of Science and Engineering, Anglia Ruskin University, Cambridge, UK
6Division of Biological Anthropology, Department of Archaeology, University of Cambridge, UK
7Department of Cognitive Biology, University of Vienna
Posted November 30, 2019.
A machine learning based approach to the segmentation of micro CT data in archaeological and evolutionary sciences
Thomas O’Mahoney, Lidija Mcknight, Tristan Lowe, Maria Mednikova, Jacob Dunn
bioRxiv 859983; doi: https://doi.org/10.1101/859983
Subject Area
Subject Areas
- Biochemistry (11695)
- Bioengineering (8714)
- Bioinformatics (29108)
- Biophysics (14918)
- Cancer Biology (12045)
- Cell Biology (17344)
- Clinical Trials (138)
- Developmental Biology (9403)
- Ecology (14133)
- Epidemiology (2067)
- Evolutionary Biology (18257)
- Genetics (12214)
- Genomics (16756)
- Immunology (11837)
- Microbiology (27983)
- Molecular Biology (11540)
- Neuroscience (60757)
- Paleontology (450)
- Pathology (1864)
- Pharmacology and Toxicology (3224)
- Physiology (4933)
- Plant Biology (10379)
- Synthetic Biology (2876)
- Systems Biology (7329)
- Zoology (1640)