GlomExtractor: a versatile tool for extracting glomerular patches from whole slide kidney biopsy images

Abstract The extraction of glomerular image patches is a key step prior to the training and application of glomerular classification models. Numerous algorithms for detecting and segmenting glomeruli have already been described in the literature, but standards for how to extract glomeruli from such an initial annotation remain lacking. Furthermore, the impact of different choices in extracting and preprocessing, such as cropping and scaling, are poorly understood and researched. To address this gap, the current paper introduces the GlomExtractor, a versatile tool implementing the key steps for extracting glomerular patches from whole slide images, including optional filtering of segmentations, patch extraction and scaling, and postprocessing such as background removal. By utilizing the GlomExtractor in combination with glomerular clustering experiments, the current study demonstrated how different processing strategies can impact the performance of downstream machine learning applications. We believe that the GlomExtractor and the experiments demonstrated in the current paper will help researchers in (i) the development of glomerular classification models, in (ii) advancing our understanding of the impact of patch processing on model performance, and in (iii) establishing community standards for how glomeruli should be extracted depending on downstream use.