Automatic glomerular identification and quantification of histological phenotypes using image analysis and machine learning.
Am J Physiol Renal Rhysiol 2018 Dec; 315:F1644-F1651
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 utilized 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 utilizes free existing software and is usable without extensive image analysis training. Validation of the classifier and feature scores in mice are highlighted in this work and show 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 is 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.
Sheehan, Susan M and Korstanje, Ron, "Automatic glomerular identification and quantification of histological phenotypes using image analysis and machine learning." (2018). Faculty Research 2018. 249.