Abstract
The presence of tubular casts within the kidney serves as an important feature when assessing the degree of renal injury. Quantification of renal tubular casts has been historically difficult due to varying cast morphologies, protein composition, and stain uptake properties, even within the same kidney. Color thresholding remains one of the most common methods of quantification in the laboratory when assessing the percentage of renal casting; however, this method is unable to account for tubule casts stained a variety of colors. We have developed a novel method of automated cast quantification using the machine learning pixel classification tool within QuPath, an open-source software designed for digital pathology. We demonstrated the usability of this method in male and female Dahl salt-sensitive rats fed either low or high salt for 2 wk and male Sprague-Dawley rats treated with podotoxin puromycin aminonucleoside (PAN). Briefly, the pixel classifier was trained to identify kidney tissue, various cast color types, and slide backgrounds. Following the development of the pixel classifier, we applied it to the sample population and compared the results with those of other methods of cast quantification, including color thresholding and manual quantification. We found that the automated pixel classifier designed in QuPath was able to comprehensively quantify metachromatic tubular casts compared with color thresholding. This novel method of cast quantification provides researchers with the ability to reliably automate cast quantification that is both comprehensive and efficient.NEW & NOTEWORTHY We developed a method of automated renal tubule cast quantification using a machine learning-based pixel classifier within QuPath, an open-source image analysis software. The advantages of this approach are demonstrated by rigorous comparison of quantification methods on a set of Masson's trichrome-stained kidney sections from high- and low-salt fed salt-sensitive Dahl rats. Researchers are provided with step-by-step instructions for creating and training a pixel classifier in QuPath for application to image analysis.