The use of deep learning to automate the segmentation of the skeleton from CT volumes of pigs

• Published in: Translational animal science Vol. 2; no. 3; pp. 324 - 335
• Main Authors: Kvam, Johannes, Gangsei, Lars Erik, Kongsro, Jørgen, Schistad Solberg, Anne H
• Format: Journal Article
• Language: English
• Published: England 01.09.2018
• Subjects: computed tomography; deep learning; pigs; image analysis
• ISSN: 2573-2102
• DOI: 10.1093/tas/txy060

Computed tomography ( ) 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.