# Large-scale automatic analysis of the OAI magnetic resonance image dataset

> **NIH NIH R01** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2021 · $460,719

## Abstract

ABSTRACT
Osteoarthritis (OA) is the most frequent form of arthritis and a common cause of disability. While OA affects
millions of people in the United States alone, joint replacement is generally the only available treatment when
the pain and disability of the disease become too great. Advances in OA research and clinical care have been
greatly hindered by a lack of sensitive biomarkers and by the absence of analysis methods for detecting such
biomarkers in some existing large datasets, such as the dataset of the Osteoarthritis Initiative (OAI).
The magnetic resonance image (MRI) dataset of the OAI contains extremely valuable longitudinal image data
from more than 4,000 subjects collected over an 8-year period. While cartilage loss is believed to be the
dominating factor in OA, to date cartilage segmentations are publicly available for only about 1% of the images
of the OAI dataset. This severely limits research on knee cartilage changes and their relation to outcome
measures. Obtaining image-based cartilage biomarkers for the full dataset is difficult, as most existing analysis
approaches are at best semi-automated. A key challenge is that the existing approaches do not scale to large
datasets: neither financially (such analysis would cost millions of dollars) nor from a practical point of view –
e.g., manually segmenting cartilage would likely require a decade of full-time work by one individual.
The aim of this project is two-fold:
1) We will invent advanced image-analysis and statistical approaches which will allow for truly large-scale
analysis of the OAI MRI dataset, i.e., will allow us to analyze the full OAI dataset. These approaches will
include methods to automatically segment and characterize knee cartilage and to assess differences between
subjects and across time. All our analysis software will be made available in open-source form to the public,
free to use for anybody. We will support custom compute clusters, cloud- and parallel computing.
2) By facilitating large-scale analysis of the entire dataset, the proposed approaches will allow us to revisit
many important clinical questions left open by gaps in prior methods. In particular, standard radiographic
outcome measures for OA progression (based on Kellgren-Lawrence grade and/or joint space narrowing) have
low reliability, are difficult to interpret, and respond poorly to change. We will therefore explore local cartilage
thickness as a measure for OA progression and its associations with putative risk factors of OA, which
(contrary to expectation) have only shown limited, conflicting, or inconclusive associations with radiographic
measures. We will also investigate the prediction of long-term OA progression from short-term cartilage
characteristics, which could help identify individuals at highest risk of rapid cartilage loss. Once identified,
these individuals could then be targeted for more aggressive therapy or for clinical trials.

## Key facts

- **NIH application ID:** 9966876
- **Project number:** 5R01AR072013-04
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Marc Niethammer
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $460,719
- **Award type:** 5
- **Project period:** 2017-08-15 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9966876

## Citation

> US National Institutes of Health, RePORTER application 9966876, Large-scale automatic analysis of the OAI magnetic resonance image dataset (5R01AR072013-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9966876. Licensed CC0.

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