Prediction of carcass rib eye area by ultrasound images in sheep using computer vision

Abstract

The present research created a tool to measure ultrasound images of the rib eye area in sheep. One hundred twenty-one ultrasound images of sheep were captured, with regions of interest segmented using the U-Net algorithm. The metrics adopted to evaluate automatic segmentations were Dicescore and intersection over union. Finally, a regression analysis was performed using the AdaBoost Regressor and Random Forest Regressor algorithms and the fit of the models was evaluated using the Mean Square Residuals, mean absolute error and coefficient of determination. The values obtained for the Dice metric were 0.94, and for Intersection over Union it was 0.89, demonstrating a high similarity between the actual and predicted values, ranging from 0 to 1. The values of Mean Quadratic Residuals, mean absolute error and coefficient The determination of the regressor models indicated the best fit for the Random Forest Regressor. The U-Net algorithm efficiently segmented ultrasound images of the Longissimus Dorsi muscle, with greater precision than the measurements performed by the specialist. This efficient segmentation allowed the standardization of rib eye area measurements and, consequently, the phenotyping of beef sheep on a large scale.