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Related Experiment Video

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Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins
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PodoCount: A Robust, Fully Automated, Whole-Slide Podocyte Quantification Tool.

Briana A Santo1, Darshana Govind1, Parnaz Daneshpajouhnejad2

  • 1Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, New York, USA.

Kidney International Reports
|June 13, 2022
PubMed
Summary

Automated podocyte counting using PodoCount accurately quantifies podocyte depletion in kidney disease research. This computational tool streamlines podometrics, improving analysis of glomerular injury and clinical outcomes.

Keywords:
chronic kidney diseasedigital pathologygigapixel size imagesglomerular diseasepodocytepodometrics

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Area of Science:

  • Nephrology
  • Computational Pathology
  • Biomedical Image Analysis

Background:

  • Podocyte depletion is a key indicator of glomerular injury and a predictor of clinical outcomes in kidney disease.
  • Current podometrics methods are semiquantitative, labor-intensive, and technically demanding.
  • High-throughput podometrics requires an automated pipeline for experimental and clinical applications.

Purpose of the Study:

  • To develop and validate PodoCount, a computational tool for automated podocyte quantification in immunohistochemically labeled kidney tissues.
  • To enable high-throughput podometrics using computational image analysis.

Main Methods:

  • Whole-slide images of murine kidney sections and human diabetic nephropathy biopsy specimens were analyzed.
  • PodoCount segmented glomeruli, extracted podocytes, and computed podocyte depletion and nuclear morphometry.
  • The tool was validated using diverse datasets and computational performance evaluation.

Main Results:

  • PodoCount achieved high accuracy in podocyte quantification, with 0.98 accuracy in identifying podocyte nuclear profiles.
  • Segmentation sensitivity and specificity were 0.85 and 0.99, respectively, with minimal errors (1 podocyte per glomerulus).
  • Image features derived from PodoCount significantly predicted disease state, proteinuria, and clinical outcomes.

Conclusions:

  • PodoCount provides high-performance, automated podocyte quantitation for diverse kidney disease models and human samples.
  • The tool's features correlate significantly with metadata and outcomes, offering a standardized approach to automated podometrics.
  • This cloud-based, open-source tool facilitates analysis of gigapixel-sized whole-slide images for researchers and clinicians.