Biomedical Informatics & Bioinformatics Service, Institute for Biomedical Research of Murcia (IMIB), 30120 Murcia, Spain.
CNRS-CEA, University Paris-Saclay, MIRCen, 92265 Paris, France.
Sensors (Basel). 2021 Mar 12;21(6):1993. doi: 10.3390/s21061993.
Liver transplantation is the only curative treatment option in patients diagnosed with end-stage liver disease. The low availability of organs demands an accurate selection procedure based on histological analysis, in order to evaluate the allograft. This assessment, traditionally carried out by a pathologist, is not exempt from subjectivity. In this sense, new tools based on machine learning and artificial vision are continuously being developed for the analysis of medical images of different typologies. Accordingly, in this work, we develop a computer vision-based application for the fast and automatic objective quantification of macrovesicular steatosis in histopathological liver section slides stained with Sudan stain. For this purpose, digital microscopy images were used to obtain thousands of feature vectors based on the RGB and CIE Lab* pixel values. These vectors, under a supervised process, were labelled as fat vacuole or non-fat vacuole, and a set of classifiers based on different algorithms were trained, accordingly. The results obtained showed an overall high accuracy for all classifiers (>0.99) with a sensitivity between 0.844 and 1, together with a specificity >0.99. In relation to their speed when classifying images, KNN and Naïve Bayes were substantially faster than other classification algorithms. Sudan stain is a convenient technique for evaluating ME in pre-transplant liver biopsies, providing reliable contrast and facilitating fast and accurate quantification through the machine learning algorithms tested.
肝移植是诊断为终末期肝病患者的唯一治愈性治疗选择。由于器官的供应有限，因此需要进行基于组织学分析的准确选择程序，以评估移植物。这种评估传统上由病理学家进行，但并非没有主观性。从这个意义上说，基于机器学习和人工视觉的新工具不断被开发出来，用于分析不同类型的医学图像。因此，在这项工作中，我们开发了一种基于计算机视觉的应用程序，用于快速自动客观量化苏丹染色的组织学肝切片中的大泡性脂肪变性。为此，使用数字显微镜图像获得了数千个基于 RGB 和 CIE Lab*像素值的特征向量。这些向量在监督过程中被标记为脂肪泡或非脂肪泡，并根据不同的算法训练了一组分类器。结果表明，所有分类器的整体准确率都很高（>0.99），敏感性在 0.844 到 1 之间，特异性>0.99。关于它们在分类图像时的速度，KNN 和朴素贝叶斯比其他分类算法快得多。苏丹染色是评估移植前肝活检中 ME 的一种方便技术，通过测试的机器学习算法提供可靠的对比，并有助于快速准确地定量。
