School of Biology & Engineering, Guizhou Medical University, Guiyang, Guizhou Province, China.
Key Laboratory of Biology and Medical Engineering, Guizhou Medical University, Guiyang, Guizhou Province, China.
J Xray Sci Technol. 2019;27(6):1033-1045. doi: 10.3233/XST-190570.
To develop and test a novel method for automatic quantification of hepatic steatosis in histologic images based on the deep learning scheme designed to predict the fat ratio directly, which aims to improve accuracy in diagnosis of non-alcoholic fatty liver disease (NAFLD) with objective assessment of the severity of hepatic steatosis instead of subjective visual estimation.
Thirty-six 8-week old New Zealand white rabbits of both sexes were fed with high-cholesterol, high-fat diet and sacrificed under deep anesthesia at various time points to obtain the pathological specimen. All rabbits were performed by multislice computed tomography for surveillance to measure density changes of liver parenchyma. A deep learning scheme using a convolutional neural network was developed to directly predict the liver fat ratio based on the pathological images. The average error value, standard deviation, and accuracy (error <5%) were evaluated and compared between the deep learning scheme and manual segmentation results. The Pearson's correlation coefficient was also calculated in this study.
The deep learning scheme performs successfully on rabbit liver histologic data, showing a high degree of accuracy and stability. The average error value, standard deviation, and accuracy (error <5%) were 3.21%, 4.02%, and 79.10% for the cropped images, 2.22%, 1.92%, and 88.34% for the original images, respectively. The strong positive correlation was also observed for cropped images (R = 0.9227) and original images (R = 0.9255) in comparison to labeled fat ratio.
This new deep learning scheme may aid in the quantification of steatosis in the liver and facilitate its treatment by providing an earlier clinical diagnosis.
开发并测试一种基于深度学习方案的新型方法，用于自动量化组织学图像中的肝脂肪变性，该方案旨在通过客观评估肝脂肪变性的严重程度而不是主观视觉估计来提高对非酒精性脂肪性肝病（NAFLD）的诊断准确性。
36 只新西兰白兔，雌雄各半，8 周龄，给予高胆固醇、高脂肪饮食，在深度麻醉下处死，获得病理标本。所有兔子均行多层螺旋 CT 监测，测量肝实质密度变化。开发了一种基于卷积神经网络的深度学习方案，根据病理图像直接预测肝脂肪比。评估和比较了深度学习方案与手动分割结果之间的平均误差值、标准差和准确率（误差<5%）。本研究还计算了 Pearson 相关系数。
深度学习方案在兔肝组织学数据上表现出色，具有很高的准确性和稳定性。裁剪图像的平均误差值、标准差和准确率（误差<5%）分别为 3.21%、4.02%和 79.10%，原始图像分别为 2.22%、1.92%和 88.34%。裁剪图像（R=0.9227）和原始图像（R=0.9255）与标记的脂肪比之间也观察到强正相关。
这种新的深度学习方案可能有助于量化肝脏脂肪变性，并通过提供早期临床诊断来促进其治疗。
