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

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

Darshana Govind et al. J Am Soc Nephrol. 2021 Nov.

Abstract

Background: Podocyte depletion precedes progressive glomerular damage in several kidney 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 the 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 GN, and dose-dependent direct podocyte toxicity and depletion, and 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.

Conclusions: 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.

Keywords: CNN; Deeplab; cloud; cloud computing; deep learning; pix2pix GAN; podocyte detection; podocytes; urinary tract; viscera.

Copyright © 2021 by the American Society of Nephrology.

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Graphical abstract
Figure 1.
Figure 1.

Schematic of the PodoSighter pipeline. (A) For training the networks, data acquisition was followed by a diverse staining protocol, wherein the acquired tissue sections were stained in one of three ways: (1) IF staining followed by PAS staining, (2) PAS staining followed by IHC staining, or (3) bleachable IHC staining followed by PAS staining. Subsequently, the stained sections were imaged into WSIs. The PAS-stained WSIs and the corresponding IF/IHC WSIs were first aligned via image registration. Next, the PAS-stained WSIs were fed to the H-AI-L pipeline to extract glomeruli locations. Subsequently, image patches containing glomeruli were extracted from the PAS WSI and the registered IF/IHC WSI. These IF/IHC image patches were converted into corresponding label images, suitable for training each network, and were fed to the respective CNN and GAN networks. (B) For testing, extracted glomeruli image patches from hold-out cases were fed to the trained networks. The predictions from the respective networks were saved and displayed as an xml file, along with the input PAS WSI. Scale bars, 2 mm.

Figure 2.
Figure 2.

Quantitative analysis of dataset-based network performances. Performance of the respective networks on distinct datasets (mouse, rat, human biopsy, and human autopsy), using the respective podocyte nuclei markers (p57 or WT1). The results were generated by testing the trained networks on approximately 0.8K mouse, approximately 1.9K rat, and 0.6K human image patches containing glomeruli.

Figure 3.
Figure 3.

CNN and GAN predictions. Podocyte detection for (A) DKD control mice and (C) diseased mice tissue sections stained using WT1, (B) Glomerulonephritis control mice and (D) diseased mice tissue sections stained using p57, (E) Puromycin nephropathy control rat and (G) diseased rat tissue sections stained using WT1, (F) Puromycin nephropathy control rat and (H) diseased rat tissue sections stained using p57, (I) human autopsy and (K) biopsy tissue sections stained using WT1, and (J) human autopsy and (L) human biopsy tissue sections stained using p57.

Figure 4.
Figure 4.

Bland–Altman plots comparing GAN (pix2pix) and CNN-based results (with respect to manual ground-truth) for podocyte volume density estimation in mice, rats, and humans. The residual plots for GAN-based (A) and CNN-based (B) podocyte nuclear densities are shown here, when compared with the method reported by Venkatareddy et al., (which is the ground-truth here). The results indicate that both networks generate similar podocyte volume densities to the ground-truth.

Figure 5.
Figure 5.

Comparison of manual and pix2pix-estimated podocyte volume densities from the PAS images in the STZ dataset. Bland–Altman plots display the agreement between the estimated podocyte volume densities (by pix2pix [shown in (A)] and the three renal annotators [A1, A2, and A3; shown in (B-D)]) and the ground-truth. The plots depict the residual error and 95% limits of agreement between the ground-truth and the estimated volume densities, calculated using the mean (µ) and 1.96 times the SD (σ) of the mean difference between the two measures. The results indicate that pix2pix displays the least absolute error and SD in podocyte volume densities when compared with the ground-truth.

Figure 6.
Figure 6.

Violin plots depicting relative podocyte loss in mouse and rat disease models. The podocyte volume densities are calculated for mouse and rat models. Here p values represent differences between controls and the respective disease models (***p<0.001), using the statistical test discussed in the Methods. The red lines indicate the difference between the average values for the control and disease cases, in the ground-truth, for the respective mouse models. The mouse models demonstrated a statistically significant podocyte depletion when compared with their control counterparts in the ground-truth and the deep learning models. In contrast, the rat models did not demonstrate a statistically significant podocyte depletion in the ground-truth or the deep learning models.

Figure 7.
Figure 7.

PodoSighter plugin on the cloud. The layout of the PodoSighter pipeline is shown here, along with a representative PAS image from a human renal biopsy. The podocyte nuclei predicted by one of the networks are highlighted in green.

Figure 8.
Figure 8.

GAN performance using different domain B images. PAS-stained glomeruli and corresponding ground-truth IF images are shown in (A) and (E) and (B) and (F), respectively. The network predictions for the two domain B images are shown in (C) and (G) (predicted IF images) and (D) and (H) (predicted in silico IF images). The arrows indicate nuclei that are faint in the predicted IF images, and hence are below the pipeline threshold for detection. The same nuclei are detected in the generated in silico images.

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