# Image Analysis Core

> **NIH NIH P30** · JACKSON LABORATORY · 2022 · $95,993

## Abstract

IMAGE ANALYSIS CORE
PROJECT SUMMARY
Evaluating changes that precede frailty and end of life using histological characterization of age-related lesions
augments molecular, cellular, and physiologic data, and provides an understanding of early-onset mechanisms
that underlie age-related changes that may eventually have clinical relevance. The overall goal of the Image
Analysis Core is to develop and provide resources for the geroscience community to aid in computer-assisted
histopathological analysis and discovery of age-related histological features. Recently, the NIA-funded
Geropathology Research Network (GRN), established to enhance the translational value of geropathology for
preclinical research studies in anti-aging clinical trials,
developed and validated a grading system, designated
the geropathology grading platform (GGP), for quantification and comparison of histological lesion scores in
tissues from aging mice. While implementation of this grading platform by a trained pathologist may be feasible
for experiments with small numbers of animals, an automated approach is necessary for experiments consisting
of large sample numbers. An automated approach that can provide unbiased analysis of large sample numbers
will lead to a more timesaving and cost-effective analysis and generation of more robust data. A quantitative
image analysis pipeline that uses machine learning to accurately identify specific features in scanned slides of
stained kidneys was recently developed. This quantitative tool can be easily adjusted to allow quantification
using the GGP. The Specific Aims of the Image Analysis Core are: Aim 1. Adapt a quantitative pipeline for
the analysis of aged heart, liver, and lung tissues by training and establishing classifiers. Currently,
scanned slides of mouse kidneys are uploaded and processed into a large number of tiles in TIF format, and
then histological features specific for the kidney are identified and automatically fed into ImageJ for quantification.
This pipeline will be adapted for aging research by introducing a training set to identify tissue-specific histological
features and develop filters for scoring the lesions according to the GGP. Aim 2. Validate the quantitative
pipeline using an annotated set of aged mouse tissues from the Geropathology Research Network. Once
pipelines specific for heart, liver, and lung are developed and trained, their accuracy and robustness will be
validated by analyzing a set of annotated slides provided by the GRN. Aim 3. Develop and distribute to the
geroscience community open-source, user-friendly packages for both the quantitative and discovery
pipelines with online training. In addition to providing image analysis as a Core service, the pipelines will be
made available to the geroscience community so that other investigators can do their own analysis and
customize the pipelines for their own research. These quantitative and discovery tools can be trained for use on
any tissue or organ an...

## Key facts

- **NIH application ID:** 10425459
- **Project number:** 5P30AG038070-13
- **Recipient organization:** JACKSON LABORATORY
- **Principal Investigator:** Ronny Korstanje
- **Activity code:** P30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $95,993
- **Award type:** 5
- **Project period:** 2010-08-15 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10425459

## Citation

> US National Institutes of Health, RePORTER application 10425459, Image Analysis Core (5P30AG038070-13). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10425459. Licensed CC0.

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