A general technique for the estimation of farm animal body part weights from CT scans and its applications in a rabbit breeding program
This work addresses a domain-specific problem in agricultural imaging for breeders, offering incremental improvements in accuracy.
The paper tackles the problem of estimating farm animal body part weights from CT scans, particularly challenging due to variable postures in non-sedated animals, and achieves a 12% higher r^2 score compared to previous techniques in rabbit breeding applications.
Various applications of farm animal imaging are based on the estimation of weights of certain body parts and cuts from the CT images of animals. In many cases, the complexity of the problem is increased by the enormous variability of postures in CT images due to the scanning of non-sedated, living animals. In this paper, we propose a general and robust approach for the estimation of the weights of cuts and body parts from the CT images of (possibly) living animals. We adapt multi-atlas based segmentation driven by elastic registration and joint feature and model selection for the regression component to cape with the large number of features and low number of samples. The proposed technique is evaluated and illustrated through real applications in rabbit breeding programs, showing r^2 scores 12% higher than previous techniques and methods that used to drive the selection so far. The proposed technique is easily adaptable to similar problems, consequently, it is shared in an open source software package for the benefit of the community.