数字农科院2.0

MPG-SwinUMamba: High-Precision Segmentation and Automated Measurement of Eye Muscle Area in Live Sheep Based on Deep Learning

文献类型： 外文期刊

作者： Zhou Zhang;Yaojing Yue;Fuzhong Li;Leifeng Guo;Svitlana Pavlova

作者机构：

关键词： automated phenotypic measurement;deep learning;image segmentation;sheep eye muscle;smart livestock management;ultrasound images

期刊名称： Animals

ISSN：

年卷期： 2025 年 15 卷 24 期

页码：

收录情况： SCIE（2025版）

摘要： Accurate EMA assessment in live sheep is crucial for genetic breeding and production management within the meat sheep industry. However, the segmentation accuracy and reliability of existing automated methods are limited by challenges inherent to B-mode ultrasound images, such as low contrast and noise interference. To address these challenges, we present MPG-SwinUMamba, a novel deep learning-based segmentation network. This model uniquely combines the state-space model with a U-Net architecture. It also integrates an edge-enhancement multi-scale attention module (MSEE) and a pyramid attention refinement module (PARM) to improve the detection of indistinct boundaries and better capture global context. The global context aggregation decoder (GCAD) is employed to precisely reconstruct the segmentation mask, enabling automated measurement of the EMA. Compared to 12 other leading segmentation models, MPG-SwinUMamba achieved superior performance, with an intersection-over-union of 91.62% and a Dice similarity coefficient of 95.54%. Additionally, automated measurements show excellent agreement with expert manual assessments (correlation coefficient r = 0.9637), with a mean absolute percentage error of only 4.05%. This method offers non-invasive and efficient and objective evaluation of carcass performance in live sheep, with the potential to reduce measurement costs and enhance breeding efficiency.

分类号：

  • 相关文献

[1]A Machine Vision-Based Method for Tea Buds Segmentation and Picking Point Location Used on a Cloud Platform. Lu, Jinzhu,Yang, Zhiming,Sun, Qianqian,Gao, Zongmei,Ma, Wei. 2023

[2]Wheat Full-Width harvesting navigation line extraction method using improved Swin-Transformer. Gong Cheng,Chengqian Jin,Man Chen,Zeyu Cai,Zheng Liu. 2025

[3]Robust Image Segmentation Method For C.otton Leaf Under Natural C onditions Based On Immune Algorithm And Pcnn Algorithm. Kong, Fantao,Zhai, Zhifen,Wu, Jianzhai,Zhang, Jianhua,Han, Shuqing. 2018

[4]Image Segmentation to HSI Model Based on Improved Particle Swarm Optimization. Zhao, Bo,Mao, Wenhua,Zhang, Xiaochao,Chen, Yajun. 2009

[5]A segmentation method for greenhouse vegetable foliar disease spots images using color information and region growing. Ma, Juncheng,Du, Keming,Zheng, Feixiang,Chu, Jinxiang,Sun, Zhongfu,Zhang, Lingxian.

[6]Image segmentation of G bands of Triticum monococcum chromosomes based on the model-based neural network. Cai, N,Hu, KH,Xiong, HT,Li, SY,Su, WF,Zhu, FS.

[7]Marbling-Net: A Novel Intelligent Framework for Pork Marbling Segmentation Using Images from Smartphones. Shufeng Zhang,Yuxi Chen,Weizhen Liu,Bang Liu,Xiang Zhou. 2023

[8]Detection method of rice blast based on 4D light field refocusing depth information fusion. Yang N.,Chang K.,Tang J.,Xu L.,He Y.,Huang R.,Yu J.. 2023

[9]An Adaptive Image Segmentation Method with Automatic Selection of Optimal Scale for Extracting Cropland Parcels in Smallholder Farming Systems. Zhiwen Cai,Qiong Hu,Xinyu Zhang,Jingya Yang,Haodong Wei,Zhen He,Qian Song,Cong Wang,Gaofei Yin,Baodong Xu. 2022

[10]ONLINE DETECTION SYSTEM FOR CRUSHED RATE AND IMPURITY RATE OF MECHANIZED SOYBEAN BASED ON DEEPLABV3+ [基于 DeepLabV3+的大豆机械化收获破碎率和含杂率在线检测系统]. Man Chen,Gong Cheng,Jinshan Xu,Guangyue Zhang,Chengqian Jin. 2023

[11]DeeplabV3+-based navigation line extraction for the sunlight robust combine harvester. Gong Cheng,Chengqian Jin,Man Chen. 2024

[12]ONLINE DETECTION SYSTEM FOR CRUSHED RATE AND IMPURITY RATE OF MECHANIZED SOYBEAN BASED ON DEEPLABV3+. Chen, Man,Cheng, Gong,Xu, Jinshan,Zhang, Guangyue,Jin, Chengqian. 2023

[13]Image Segmentation-Based Oilseed Rape Row Detection for Infield Navigation of Agri-Robot. Guoxu Li,Feixiang Le,Shuning Si,Longfei Cui,Xinyu Xue. 2024

[14]Research on Innovative Apple Grading Technology Driven by Intelligent Vision and Machine Learning. Bo Han,Jingjing Zhang,Rolla Almodfer,Yingchao Wang,Wei Sun,Tao Bai,Luan Dong,Wenjing Hou. 2025

[15]A counting method of whiteflies on crop leave images captured by AR glasses based on segmentation and improved YOLOv11 models. Shilong Zhao,Jun Lyu,Shuhua Liu,Zelin Feng,Heping Ling,Jiabao Jiao,Zhaoxin Ni,Baojun Yang,Qing Yao,Ju Luo. 2025

[16]FEL-YoloV8: A New Algorithm for Accurate Monitoring Soybean Seedling Emergence Rates and Growth Uniformity. Yu, Xun,Jiang, Tiantian,Zhu, Yanqin,Li, Liming,Fan, Fan,Jin, Xiuliang. 2025

[17]Deep learning based weed detection and target spraying robot system at seedling stage of cotton field. Xiangpeng Fan,Xiujuan Chai,Jianping Zhou,Tan Sun. 2023

[18]Inter-Continental Transfer of Pre-Trained Deep Learning Rice Mapping Model and Its Generalization Ability. Lingbo Yang,Ran Huang,Jingcheng Zhang,Jingfeng Huang,Limin Wang,Jiancong Dong,Jie Shao. 2023

[19]Semantic Segmentation Based on Temporal Features: Learning of Temporal-Spatial Information From Time-Series SAR Images for Paddy Rice Mapping. Yang, Lingbo,Huang, Ran,Huang, Jingfeng,Lin, Tao,Wang, Limin,Mijiti, Ruzemaimaiti,Wei, Pengliang,Tang, Chao,Shao, Jie,Li, Qiangzi,Du, Xin. 2021

[20]A Novel Physics-Statistical Coupled Paradigm for Retrieving Integrated Water Vapor Content Based on Artificial Intelligence. Ruyu Mei,Kebiao Mao,Jiancheng Shi,Jeffrey Nielson,Sayed M. Bateni,Fei Meng,Guoming Du. 2023

作者其他论文 更多>>