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

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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.