Articles | Volume 40, issue 2
https://doi.org/10.5194/jm-40-163-2021
https://doi.org/10.5194/jm-40-163-2021
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: 1,330 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,022 273 35 1,330 38 29
  • HTML: 1,022
  • PDF: 273
  • XML: 35
  • Total: 1,330
  • BibTeX: 38
  • EndNote: 29
Views and downloads (calculated since 22 Oct 2021)
Cumulative views and downloads (calculated since 22 Oct 2021)

Viewed (geographical distribution)

Total article views: 1,244 (including HTML, PDF, and XML) Thereof 1,244 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 02 Jul 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.