  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.
