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Research Note: A deep learning method segments chicken keel bones from whole-body X-ray images.

Moh Sallam¹, Samuel Coulbourn Flores², Dirk Jan de Koning¹

¹Department of Animal Biosciences, Swedish University of Agricultural Sciences, Box 7023, 750 07, Uppsala, Sweden.

Poultry Science

|August 27, 2024

Summary

Automating keel bone health monitoring in laying hens is now possible. A deep learning model accurately segments the keel bone from X-ray images, enabling future fracture risk assessment.

Keywords:

keel bone laying hen machine deep learning segmentation sternum

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Area of Science:

Veterinary Radiology
Animal Science
Biomedical Imaging

Background:

Commercial laying hens frequently experience sternum (keel) bone damage, including deviations and fractures.
Accurate assessment of keel bone condition is crucial for animal welfare and productivity.

Purpose of the Study:

To develop and train a deep learning model for automatic segmentation of the keel bone from whole-body X-ray images of laying hens.
To establish a foundation for automated measurements of keel bone geometry and density.

Main Methods:

Acquisition of 1,051 full-body X-ray images from laying hens.
Manual outlining of the keel bone on each image for annotation.
Training a U-net deep learning model for keel bone segmentation.
Evaluation using 5-fold cross-validation.

Main Results:

The trained U-net model achieved high and repeatable segmentation accuracy, with Dice coefficients ranging from 0.88 to 0.90.
The model demonstrated robust performance across multiple validation folds.

Conclusions:

Automatic segmentation of the laying hen keel bone from X-ray images is feasible with high accuracy.
Accurate segmentation is a prerequisite for automated measurements, facilitating the connection between bone characteristics and fracture susceptibility.