In vivo prediction of intramuscular fat using ultrasound and deep learning
In vivo prediction of intramuscular fat using ultrasound and deep learning
复制标题
使用超声波和深度学习体内预测肌内脂肪
DOI：
10.1016/j.compag.2017.11.020
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发表时间：
2017
影响因子：
8.3
通讯作者：
J. Kongsro
中科院分区：
文献类型：
--
作者：
Johannes Kvam;J. Kongsro
猪的肌内脂肪（IMF）决定了肉的多汁性和吸引力。本文提出了一种在超声图像上使用深度卷积神经网络估计IMF的非侵入性体内方法。该方法在中低IMF图像上表现最好，< 6%，相关性R= 0.82，均方根误差RMSE= 1.2。在较高的IMF含量下，由于图像质量和缺乏训练数据，卷积神经网络无法泛化。
Intramuscular fat (IMF) in pigs determines the succulency and attractiveness of the meat. This paper presents a non-invasive in vivo method for estimating IMF using deep convolutional neural networks on ultrasound images. The method performs best on moderate to low IMF images< 6% giving a correlation of R= 0.82 and root-mean-square-error RMSE= 1.2. At higher IMF content the convolutional neural network fails to generalize due to image quality and lack of training data.