Automatic glomerular identification and quantification of histological phenotypes using image analysis and machine learning.
Automatic glomerular identification and quantification of histological phenotypes using image analysis and machine learning.
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使用图像分析和机器学习自动肾小球识别和组织学表型量化。
DOI：
10.1152/ajprenal.00629.2017
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Korstanje,Ron
中科院分区：
文献类型：
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作者：
Sheehan,SusanM;Korstanje,Ron
目前对组织学肾脏样品，特别是肾小球进行评分的方法不允许以高通量和一致的方式收集定量数据。未经训练的个人和计算机目前都不能识别肾小球特征，因此专家病理学家必须使用分类矩阵进行识别和评分，这使得统计分析复杂化。这些样本中编码了有关整体健康和生理的关键信息。快速综合组织学评分可与其他生理指标结合使用，以显著推进肾脏研究。因此，我们使用机器学习开发了一种高通量方法来自动识别和收集肾小球的定量数据。我们的方法需要最少的步骤之间的人为交互，并提供独立于用户偏见的可量化数据。该方法使用免费的现有软件，无需大量的图像分析培训即可使用。在小鼠中的分类器和特征分数的验证在这项工作中突出显示，并显示了在小鼠研究中应用这种方法的力量。初步结果表明，该方法可以应用于相关数据训练后，从不同物种的数据集，允许快速肾小球识别和肾小球特征的定量测量。在这项工作中突出了分类器和特征分数的验证，并显示了应用这种方法的能力。由此产生的数据没有用户偏见。连续的数据，使得可以进行统计分析，允许更精确和全面的询问样品。然后，这些数据可以与其他生理数据相结合，以扩大我们对肾功能的整体理解。
Current methods of scoring histological kidney samples, specifically glomeruli, do not allow for collection of quantitative data in a high-throughput and consistent manner. Neither untrained individuals nor computers are presently capable of identifying glomerular features, so expert pathologists must do the identification and score using a categorical matrix, complicating statistical analysis. Critical information regarding overall health and physiology is encoded in these samples. Rapid comprehensive histological scoring could be used, in combination with other physiological measures, to significantly advance renal research. Therefore, we used machine learning to develop a high-throughput method to automatically identify and collect quantitative data from glomeruli. Our method requires minimal human interaction between steps and provides quantifiable data independent of user bias. The method uses free existing software and is usable without extensive image analysis training. Validation of the classifier and feature scores in mice is highlighted in this work and shows the power of applying this method in murine research. Preliminary results indicate that the method can be applied to data sets from different species after training on relevant data, allowing for fast glomerular identification and quantitative measurements of glomerular features. Validation of the classifier and feature scores are highlighted in this work and show the power of applying this method. The resulting data are free from user bias. Continuous data, such that statistical analysis can be performed, allows for more precise and comprehensive interrogation of samples. These data can then be combined with other physiological data to broaden our overall understanding of renal function.