# A Cloud Based Distributed Tool for Computational Renal Pathology

> **NIH NIH R21** · UNIVERSITY OF FLORIDA · 2023 · $202,088

## Abstract

Quantitative computational analysis of digital pathology whole slide images (WSIs) has shown increasing
promise for precision medicine applications in last decade. In recent years, this progress has extended to renal
pathology, while seeing a growing need of objective quantification of deep features from large digital WSIs of
renal tissues. The current standard of routine brightfield visual assessment of renal biopsies is unable to elicit
and quantify the deep features from large WSIs elicited by machine vision techniques. Existing computational
renal pathology tools primarily focus on extraction of renal micro-compartments and computational classification
of renal diseases. However, understanding the correlation between deep features of renal micro-compartments
and clinical biometrics and correlations with molecular level data remain as opportunities for investigation and
discovery. A major gap that needs to be closed is that the tools developed by computational researchers are not
in a format that can be easily implemented by pathology end-users. The availability of plug-and-play tools will
empower renal pathologists and biologists engaged in kidney research, and offer exponential growth in research
studies using increasingly available digital datasets across various kidney diseases via consortia including the
Kidney Precision Medicine Project, Nephrotic Syndrome Study Network, Cure Glomerulonephropathy, and
Human Biomolecular Atlas Project. To address the above gap, experts from computational imaging (Dr. Sarder),
software science (Mr. Manthey), nephropathology/basic science (Dr. Rosenberg), and nephrology (Dr. Han)
have teamed up to develop a web-cloud based end-user software for nephropathologists, nephrologists, and
basic scientists. The proposed tool emerges from ongoing collaboration between the team members. The
proposed software will offer the following functionalities to renal pathology end-users: (i) cloud storage and
visualization of digital renal pathology WSIs and associated metadata; (ii) microanatomic/histomorphologic
annotation capability in an easy-to-use web-based visualization system, allowing users to collaborate while
conducting annotation; (iii) automated plug-and-play plugins that would allow users to segment multi-scale renal
structures for a large batch of renal tissue WSIs, and (iv) plugins to refine renal micro-compartmental
segmentation in a human-artificial-intelligence-loop set-up where humans and AI system collaborate in the cloud
to refine the segmentation models iteratively; and finally, (v) measurement of deep image features on the
segmented renal structures to enable diagnostic and prognostic research for the spectrum of renal diseases.
The distributed tool will facilitate multi-center studies using federated learning where individual centers will not
need to export data with protected healthcare information outside their institutes, while still participating in training
the proposed system to improve segmenta...

## Key facts

- **NIH application ID:** 10594498
- **Project number:** 5R21DK128668-03
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Pinaki Sarder
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $202,088
- **Award type:** 5
- **Project period:** 2022-08-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10594498

## Citation

> US National Institutes of Health, RePORTER application 10594498, A Cloud Based Distributed Tool for Computational Renal Pathology (5R21DK128668-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10594498. Licensed CC0.

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