PodoSighter: A Cloud-Based Tool for Label-Free Podocyte Detection in Kidney Whole-Slide Images

Abstract

Background: Podocyte depletion precedes progressive glomerular damage in several renal diseases. However, the current standard of visual detection and quantification of podocyte nuclei from brightfield microscopy images is laborious and imprecise. Methods: We have developed PodoSighter, an online cloud-based tool, to automatically identify and quantify podocyte nuclei from giga-pixel brightfield whole-slide images (WSIs) using deep learning. Ground-truth to train the tool used immunohistochemically or immunofluorescence-labeled images from a multi-institutional cohort of 122 histologic sections from mouse, rat, and human kidneys. To demonstrate generalizability of our tool in investigating podocyte loss in clinically relevant samples, we tested it in rodent models of glomerular diseases, including diabetic kidney disease, crescentic glomerulonephritis, and dose-dependent direct podocyte toxicity and depletion, as well as in human biopsies from steroid resistant nephrotic syndrome and from human autopsy tissues. Results: The optimal model yielded high sensitivity/specificity of 0.80/0.80, 0.81/0.86, and 0.80/0.91, in mouse, rat, and human images, respectively, from periodic-acid Schiff-stained WSIs. Furthermore, the podocyte nuclear morphometrics extracted using PodoSighter were informative in identifying diseased glomeruli. We have made PodoSighter freely available to the general public as turnkey plugins in a cloud-based web application for end-users. Conclusion: Our study demonstrates an automated computational approach to detect and quantify podocyte nuclei in standard histologically-stained WSIs, facilitating podocyte research and enabling possible future clinical applications.

PodoCount: A Robust, Fully Automated, Whole-Slide Podocyte Quantification Tool

“…Prior studies validated that podocyte estimation from single sections is comparable to 3-dimensional, multisection methods, irrespective of disease state. 3 , 12 , 14 , 15 , 16 Murine data CFs and podocyte density estimates computed using PodoCount aligned well with the literature, 16 , 50 , 57 , 58 , 59 as did human CF values. 16 Podocyte density estimates for human DN data were slightly lower than those values reported for prior studies in early diabetes and clinical nephropathy.…”

“…In this work, we introduced PodoCount, a novel tool for automated, whole-slide assessment of podocyte depletion and nuclear morphometry. Compared with existing automated methods, 50 , 51 PodoCount takes a deterministic approach to podocyte quantification, using classical image analysis in place of deep learning. Benefits of this approach include computational simplicity, strong performance without big-data requirements, and thus no need for expensive GPUs or training time.…”

The potential of artificial intelligence-based applications in kidney pathology

“…ANCA-GN patients could be identified in a cohort containing ACNA-GN patients and controls based on their derived podocyte-metrics. A similar approach applicable to rodent kidney tissue was recently published and made available as a free online tool [23 ▪▪ ]. Also in this study, a model-derived metric (podocyte-density) helped identify diseased glomeruli.…”

User-Accessible Machine Learning Approaches for Cell Segmentation and Analysis in Tissue

“…A limitation of this approach is the requirement for on-going support for personnel; on-going support is critical to long term success. To ease the generation of high-quality training data with a “human-in-the-loop” approach, methods have also been established around segmentation refinement ( Sullivan et al, 2018 ; Lutnick et al, 2019 ; Moen et al, 2019 ; Govind et al, 2021 ; Lee et al, 2021 ). An alternative to these brute-force approach has been to generate synthetic training data by combining “blob” models of cells with real images using generative adversarial networks ( Dunn et al, 2019 ; Wu et al, 2021 ).…”