Automated assessment of glomerulosclerosis and tubular atrophy using deep learning. (June 2021)

• Record Type: Journal Article
• Authors: Salvi, Massimo; Mogetta, Alessandro; Gambella, Alessandro; Molinaro, Luca; Barreca, Antonella; Papotti, Mauro; Molinari, Filippo
• Is Part Of: Computerized medical imaging and graphics. Volume 90(2021)
• Publication Date: 2021-06
• Subjects: Glomeruli segmentation -- Tubular atrophy -- Digital pathology -- Kidney histology -- Deep learning; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2021.101930
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: Architecture of glomeruli and tubules play a crucial role during transplantations. Histological assessment is subjected to inter- and intra-observer variability. A new deep learning method is presented for kidney histopathological images. A Dice of 95.29 % and 91.74 % was achieved for glomeruli and tubules, respectively. Our approach could improve the diagnostic workflow in kidney transplantation. Abstract: In kidney transplantations, pathologists evaluate the architecture of both glomeruli, interstitium and tubules to assess the nephron status. An accurate assessment of glomerulosclerosis and tubular atrophy is crucial for determining kidney acceptance, which is currently based on the pathologists' histological evaluations on renal biopsies in addition to clinical data. In this work, we present an automated algorithm, called RENTAG (Robust EvaluatioN of Tubular Atrophy & Glomerulosclerosis), for the segmentation and classification of glomerular and tubular structures in histopathological images. The proposed novel strategy combines the accuracy of a level-set with the semantic segmentation of convolutional neural networks to detect the glomeruli and tubules contours. In the TEST set, our method exhibited excellent performance in both glomeruli (dice score: 0.9529) and tubule (dice score: 0.9174) detection and outperformed all the compared methods. To the best of our knowledge, the RENTAG algorithm is the first fully automated method capable of quantifying glomerulosclerosis and tubular atrophy in digital histological images. The developed software can be employed for the analysis of pre-transplantation biopsies to support the pathologists' diagnostic activity.