Abstract

Computed tomography (CT) scanning of pigs has been shown to produce detailed phenotypes useful in pig breeding. Due to the large number of individuals scanned and corresponding large data sets, there is a need for automatic tools for analysis of these data sets. In this paper, the feasibility of deep learning for fully automatic segmentation of the skeleton of pigs from CT volumes is explored. To maximize performance, given the training data available, a series of problem simplifications are applied. The deep-learning approach can replace our currently used semiautomatic solution, with increased robustness and little or no need for manual control. Accuracy was highly affected by training data, and expanding the training set can further increase performance making this approach especially promising.

Details

Title
The use of deep learning to automate the segmentation of the skeleton from CT volumes of pigs
Author
Kvam, Johannes 1 ; Gangsei, Lars Erik 2 ; Kongsro, Jørgen 3 ; Solberg, Anne H Schistad 4 

 Norsvin SA, Hamar, Norway; Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway 
 Animalia, Oslo, Norway; Norwegian University of Life Sciences (NMBU), Department of Biostatistics, Ås, Norway 
 Norsvin SA, Hamar, Norway 
 Department of Informatics, University of Oslo, Oslo, Norway 
Pages
324-335
Publication year
2018
Publication date
Sep 2018
Publisher
Oxford University Press
e-ISSN
25732102
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3171753087
Copyright
© The Author(s) 2018. Published by Oxford University Press on behalf of the American Society of Animal Science. This work is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.