A Deep Learning-Based Tool for Segmentation and Quantification of i-IFTA, Focal Infiltrates, and Tubular Dilation in Haematoxylin–Eosin-Stained Renal Whole-Slide Images

(1) Background: Simultaneous evaluation of acute and chronic lesions is critical for staging renal disease, yet manual assessment suffers from high inter-observer variability and heavy workloads. While deep learning models excel at structural segmentation, none currently consolidate the concurrent quantification of key AKI-to-CKD transition markers on standard-stained tissue slides. (2) Methods: We developed a U-Net-based convolutional neural network for the pixel-wise segmentation and quantification of inflammation-associated interstitial fibrosis and tubular atrophy (i-IFTA), focal inflammatory infiltrates and tubular dilation in haematoxylin–eosin-stained whole-slide images (WSIs) from rat models of renal injury. (3) Results: The model demonstrated robust pixel-wise segmentation in H&E-stained rodent renal tissue (overall accuracy: 88.26%, macro-averaged Dice coefficient: 0.843; per-class Dice ranging from 0.727 for focal infiltrates to 0.920 for unaltered tissue). While comparative evaluation against human annotators was performed on a limited patch subset, the model exhibited agreement trends close to expert pathologists and superior to junior annotators. The end-to-end WSI inference pipeline generates full-slide visual overlays and automated WSI-level quantitative area measurements in a mean time of 103.57 s per WSI on GPU hardware. (4) Conclusions: This model offers an objective and automated framework for quantifying key structural and inflammatory lesions in rodent renal tissue. By enabling rapid WSI-level quantitative area measurements, it establishes a solid foundation for preclinical histopathological assessment and provides high potential for future translation into human clinical pathology.