Image Analysis for Non-Neoplastic Kidney Disease: Utilizing Morphological Segmentation to Improve Quantification of Interstitial Fibrosis

Abstract

Interstitial fibrosis (IF) is a hallmark of chronic kidney disease (CKD) and a strong predictor of progression to end-stage kidney disease (ESKD). Current biopsy-based IF assessments rely on subjective visual estimations, limiting reproducibility. Sirius Red staining is widely used for visualizing fibrotic tissue, yet its application in digital pathology is limited by non-specific staining. This study investigates the impact of cortical structure segmentation on fibrosis quantification in Sirius Red-stained, non-neoplastic kidney biopsies. Fibrosis measurements before and after segmentation were compared using two image analysis methods (stain deconvolution and red-green), with ground truth fibrosis measured by point counting and expert pathologist grading. Excluding non-interstitial structures led to a significant reduction of quantified fibrosis and improved correlation with pathology grading and point counting for both the stain deconvolution and the red green method. Bland-Altman analysis showed reduced bias after segmentation: for the deconvolution method, mean difference decreased from +2.5% (95% LoA: -8% to +13%) to +1% (-7% to +9%); for the red-green method, from +3% (-10% to +16%) to +1% (-8% to +10%). Correlation with pathology grading also improved (Spearmans {rho} rose from 0.55 to 0.58 for deconvolution and from 0.53 to 0.59 for red-green). These findings confirm that targeted segmentation enhances the accuracy and consistency of automated fibrosis assessment, supporting its integration into digital pathology workflows as a critical step toward reliable quantification of fibrosis in kidney disease.