Marbling characteristics are important traits for the genetic improvement of pork quality. Accurate marbling segmentation is the prerequisite for the quantification of these traits. However, the marbling targets are small and thin with dissimilar sizes and shapes and scattered in pork, complicating the segmentation task. Here, we proposed a deep learning-based pipeline, a shallow context encoder network (Marbling-Net) with the usage of patch-based training strategy and image up-sampling to accurately segment marbling regions from images of pork longissimus dorsi (LD) collected by smartphones. A total of 173 images of pork LD were acquired from different pigs and released as a pixel-wise annotation marbling dataset, the pork marbling dataset 2023 (PMD2023). The proposed pipeline achieved an IoU of 76.8%, a precision of 87.8%, a recall of 86.0%, and an F1-score of 86.9% on PMD2023, outperforming the state-of-art counterparts. The marbling ratios in 100 images of pork LD are highly correlated with marbling scores and intramuscular fat content measured by the spectrometer method (R2 = 0.884 and 0.733, respectively), demonstrating the reliability of our method. The trained model could be deployed in mobile platforms to accurately quantify pork marbling characteristics, benefiting the pork quality breeding and meat industry.

中文翻译：

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Marbling-Net：一种使用智能手机图像进行猪肉大理石花纹分割的新型智能框架

大理石花纹特性是猪肉品质遗传改良的重要性状。准确的大理石花纹分割是量化这些特征的先决条件。然而，大理石花纹目标又小又薄，大小和形状不同，散布在猪肉中，使分割任务复杂化。在这里，我们提出了一种基于深度学习的管道，一种浅层上下文编码器网络 (Marbling-Net)，使用基于补丁的训练策略和图像上采样从采集的猪肉背最长肌 (LD) 图像中准确分割大理石花纹区域通过智能手机。从不同的猪身上采集了总共 173 张猪肉 LD 图像，并作为像素级注释大理石花纹数据集发布，即猪肉大理石花纹数据集 2023 (PMD2023)。拟议的管道实现了 76.8% 的 IoU，87.8% 的精度，86.0% 的召回率，PMD2023 的 F1 分数为 86.9%，优于最先进的同行。100 幅猪肉 LD 图像中的大理石花纹比率与通过光谱仪方法测量的大理石花纹分数和肌内脂肪含量高度相关（分别为 R2 = 0.884 和 0.733），证明了我们方法的可靠性。训练后的模型可以部署在移动平台上，准确量化猪肉大理石纹特征，造福猪肉品质育种和肉类行业。