Post-Graduate Program of Pharmaceutical Sciences University, Vila Velha, ES, Brazil.
Post-Graduate Program of Pharmaceutical Sciences University, Vila Velha, ES, Brazil.
Tissue Cell. 2019 Jun;58:12-16. doi: 10.1016/j.tice.2019.03.002. Epub 2019 Mar 27.
In the early 1950s, flow cytometry was developed as the first method for automated quantitative cellular analysis. In the early 1990s, the first equipment for image cytometry (laser scanning cytometry, LSC) became commercially available. As flow cytometry was considered the gold standard, various studies found that the results of flow cytometry and LSC generated comparable results. One of the first programs for image analysis that included morphological parameters was ImageJ, published in 1997. One of the newer programs for image analysis that is not limited to fluorescence images is the free software CellProfiler. In 2008, the same group published a new software, CellProfiler Analyst. One part of CellProfiler Analyst is a supervised machine-learning-based classifier that allows users to conduct imaging-based diagnoses, e.g., cellular diagnosis based on morphology. Another relatively new, free software for image analysis is QuPath. The aim of the present study was to compare two free programs for conducting image analysis, CellProfiler and QuPath, and the subsequent classification based on machine learning. For this study, images of renal tissue were analyzed, and the identified objects were classified. The same images were loaded in both software programs. Advanced statistical analysis was used to compare the two methods. The Bland-Altman assay showed that all of the differences were within the mean ± 1.96 * standard deviation, i.e., the differences are normally distributed, and the software programs are comparable. For the analyzed samples (renal tissue stained with HIF and TUNEL), the use of QuPath was easier because it offers image analysis without a previous processing of the images (e.g., conversion to grayscale, inverted intensities) and an unsupervised machine learning process.
在 20 世纪 50 年代早期，流式细胞术被开发为第一个自动定量细胞分析方法。在 20 世纪 90 年代早期，第一台图像细胞仪（激光扫描细胞仪，LSC）设备开始商业化。由于流式细胞术被认为是金标准，因此各种研究发现流式细胞术和 LSC 的结果产生了可比的结果。第一个包含形态学参数的图像分析程序之一是 1997 年发布的 ImageJ。另一个较新的不限于荧光图像的图像分析程序是免费软件 CellProfiler。2008 年，同一组发布了一个新软件，CellProfiler Analyst。CellProfiler Analyst 的一部分是基于监督机器学习的分类器，允许用户进行基于成像的诊断，例如基于形态学的细胞诊断。另一个相对较新的免费图像分析软件是 QuPath。本研究的目的是比较两种用于进行图像分析的免费程序，CellProfiler 和 QuPath，以及随后基于机器学习的分类。为此研究，分析了肾组织的图像，并对识别出的对象进行了分类。两种软件程序都加载了相同的图像。使用高级统计分析比较了两种方法。Bland-Altman 检验表明，所有差异均在均值±1.96*标准差范围内，即差异呈正态分布，两种软件程序具有可比性。对于分析的样本（用 HIF 和 TUNEL 染色的肾组织），使用 QuPath 更容易，因为它提供了无需对图像进行预处理（例如转换为灰度、反转强度）和无监督机器学习过程的图像分析。
