Diagnosing liver steatosis is an essential precaution for detecting hepatocirrhosis and liver cancer in the early stages. However, automatic diagnosis of liver steatosis from ultrasound (US) images remains challenging due to poor visual quality from various origins, such as speckle noise and blurring. In this paper, we propose a fully automated liver steatosis prediction model using three deep learning neural networks. As a result, liver steatosis can be automatically detected with high accuracy and precision. First, transfer learning is used for semantically segmenting the liver and kidney (L-K) on parasagittal US images, and then cropping the L-K area from the original US images. The second neural network also involves semantic segmentation by checking the presence of a ring that is typically located around the kidney and cropping of the L-K area from the original US images. These cropped L-K areas are inputted to the final neural network, SteatosisNet, in order to grade the severity of fatty liver disease. The experimental results demonstrate that the proposed model can predict fatty liver disease with the sensitivity of 99.78%, specificity of 100%, PPV of 100%, NPV of 99.83%, and diagnostic accuracy of 99.91%, which is comparable to the common results annotated by medical experts.

中文翻译：

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使用超声图像自动诊断肝脏脂肪变性的级联深度学习神经网络

诊断肝脂肪变性是早期发现肝硬化和肝癌的重要预防措施。然而，由于各种来源的视觉质量不佳，例如斑点噪声和模糊，从超声 (US) 图像自动诊断肝脏脂肪变性仍然具有挑战性。在本文中，我们提出了一种使用三个深度学习神经网络的全自动肝脏脂肪变性预测模型。结果，可以高精度和精确地自动检测肝脏脂肪变性。首先，迁移学习用于对矢状面超声图像上的肝脏和肾脏 (LK) 进行语义分割，然后从原始超声图像中裁剪 LK 区域。第二个神经网络还涉及语义分割，通过检查通常位于肾脏周围的环的存在并从原始美国图像中裁剪 LK 区域。这些裁剪的 LK 区域被输入到最终的神经网络 SteatosisNet，以对脂肪肝疾病的严重程度进行分级。实验结果表明，所提出的模型能够以99.78%的敏感性、100%的特异性、100%的PPV、99.83%的NPV和99.91%的诊断准确率预测脂肪肝，与标注的常见结果相当由医学专家。