A general technique for the estimation of farm animal body part weights from CT scans and its applications in a rabbit breeding program

TL;DR

This paper introduces a robust, adaptable method for estimating farm animal body part weights from CT scans, effectively handling variability in posture and applied successfully in rabbit breeding, with improved accuracy over previous methods.

Contribution

It presents a novel, general approach combining multi-atlas segmentation and elastic registration for accurate weight estimation from CT images of live animals.

Findings

r^2 scores 12% higher than previous methods

Effective in handling variability in animal posture

Open source implementation available

Abstract

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.