Journal of Micropalaeontology
Journal of Micropalaeontology
JM
Articles | Volume 40, issue 2
Articles | Volume 40, issue 2
https://doi.org/10.5194/jm-40-163-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/jm-40-163-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 40, issue 2
Research article
 | 
22 Oct 2021
Research article |  | 22 Oct 2021

Semantic segmentation of vertebrate microfossils from computed tomography data using a deep learning approach

Yemao Hou, Mario Canul-Ku, Xindong Cui, Rogelio Hasimoto-Beltran, and Min Zhu

Viewed

Total article views: 1,182 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
889 263 30 1,182 32 24
  • HTML: 889
  • PDF: 263
  • XML: 30
  • Total: 1,182
  • BibTeX: 32
  • EndNote: 24
Views and downloads (calculated since 22 Oct 2021)
Cumulative views and downloads (calculated since 22 Oct 2021)

Viewed (geographical distribution)

Total article views: 1,102 (including HTML, PDF, and XML) Thereof 1,102 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 23 Apr 2024
Download
Short summary
In this study, we constructed an open dataset, which contains computed tomography (CT) data on nearly 500 vertebrate microfossils. We propose a semantic segmentation method for CT fish microfossil data based on deep learning (DL). We expect that our proposed method could be applied to CT data on other fossils with good performance. We also believe the fast-accumulating CT data on vertebrate microfossils might become a source of information-rich datasets for deep learning.
Read more