Department of Animal Biosciences, Swedish University of Agricultural Sciences, Box 7023, 750 07, Uppsala, Sweden.
Department of Animal Biosciences, Swedish University of Agricultural Sciences, Box 7023, 750 07, Uppsala, Sweden; Department of Biochemistry and Biophysics, Stockholm University, Tomtebodavägen 23A, 171 65, Solna, Sweden.
Poult Sci. 2024 Nov;103(11):104214. doi: 10.1016/j.psj.2024.104214. Epub 2024 Aug 13.
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.
大多数商业产蛋鸡都患有胸骨（龙骨）骨损伤，包括变形和骨折。对母鸡进行 X 光检查，然后对龙骨进行分段和评估，是实现龙骨状况自动监测的关键。本研究的目的是训练深度学习模型，以便从全身 X 光图像中分割出龙骨。我们获得了产蛋母鸡的全身 X 光图像（n=1051），并在每张图像上手动绘制了龙骨的轮廓。然后，使用标注图像训练 U-net 模型以分割龙骨。该模型使用 5 折交叉验证进行评估。我们在几个验证折中的重复结果都得到了很高的分割准确性（Dice 系数为 0.88-0.90）。总之，从全身 X 光图像中自动分割龙骨是可能的，并且具有良好的准确性。分割是对龙骨几何形状和密度进行自动测量的前提，这可以进一步与龙骨变形和骨折的易感性联系起来。
