ArticlePloS one2023
CT image segmentation of meat sheep Loin based on deep learning.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A Narrative Review of Early Pregnancy Diagnosis Technologies for Livestock: From Conventional to Intelligent Systems.Animals : an open access journal from MDPI · 2026Review
- High-throughput in vivo phenotyping of chicken carcass traits based on digital radiographic imaging.Journal of animal science and biotechnology · 2026Article
- Multi-Scale Feature Fusion and Attention-Enhanced R2U-Net for Dynamic Weight Monitoring of Chicken Carcasses.Animals : an open access journal from MDPI · 2026Article
- Article
- MPG-SwinUMamba: High-Precision Segmentation and Automated Measurement of Eye Muscle Area in Live Sheep Based on Deep Learning.Animals : an open access journal from MDPI · 2025Article
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Authors and funding
9 authors.
Funding
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Abstract
There are no clear boundaries between internal tissues in sheep Computerized Tomography images, and it is difficult for traditional methods to meet the requirements of image segmentation in application. Deep learning has shown excellent performance in image analysis. In this context, we investigated the Loin CT image segmentation of sheep based on deep learning models. The Fully Convolutional Neural Network (FCN) and 5 different UNet models were applied in image segmentation on the data set of 1471 CT images including the Loin part from 25 Australian White rams and Dolper rams using the method of 5-fold cross validation. After 10 independent runs, different evaluation metrics were applied to assess the performances of the models. All models showed excellent results in terms evaluation metrics. There were slight differences among the results from the six models, and Attention-UNet outperformed others methods with 0.998±0.009 in accuracy, 4.391±0.338 in AVER_HD, 0.90±0.012 in MIOU and 0.95±0.007 in DICE, respectively, while the optimal value of LOSS was 0.029±0.018 from Channel-UNet, and the running time of ResNet34-UNet is the shortest.
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