Skip to main content
SLU publication database (SLUpub)

Research article2024Peer reviewedOpen access

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

Sallam, Moh; Flores, Samuel Coulbourn; de Koning, Dirk Jan; Johnsson, Martin

Abstract

Most commercial laying hens suffer from sternum (keel) bone damage including deviations and fractures. X-raying hens, followed by segmenting and assessing the keel bone, is a key to automating the monitoring of keel bone condition. The aim of the current work is to train a deep learning model to segment the keel bone out of whole-body x-ray images. We obtained full-body x-ray images of laying hens (n = 1,051) and manually drew the outline of the keel bone on each image. Using the annotated images, a U-net model was then trained to segment the keel bone. The proposed model was evaluated using 5-fold cross validation. We obtained high segmentation accuracy (Dice coefficients of 0.88-0.90) repeatably over several validation folds. In conclusion, automatic segmentation of the keel bone from full-body x-ray images is possible with good accuracy. Segmentation is a requirement for automated measurements of keel geometry and density, which can subsequently be connected to susceptibility to keel deviations and fractures.

Keywords

keel bone; sternum; machine deep learning; segmentation; laying hen

Published in

Poultry Science
2024, Volume: 103, number: 11, article number: 104214