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

OAI磁共振图像数据集的大规模自动分析

• 批准号: 9368542
• 负责人: Marc Niethammer
• 金额: $ 42.23万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9368542
• 关键词: Accounting; Affect; Appearance; Arthritis; Atlases; Biochemical; Biological Markers; Cartilage; Characteristics; Charge; Clinical; Clinical Trials; Cloud Computing; Cohort Studies; Communities; Computer software; Conflict (Psychology); Custom; Data; Data Analyses; Data Set; Databases; Degenerative polyarthritis; Diagnostic radiologic examination; Disease; Disease Marker; Early identification; Future; Heterogeneity; Hospitals; Hour; Image; Image Analysis; Imaging technology; Individual; Joints; Kellgren-Lawrence grade; Knee; Knee Osteoarthritis; Left; Lesion; Longitudinal Studies; Magnetic Resonance Imaging; Manuals; Measures; Methods; Morbidity - disease rate; Morphology; Organ; Outcome; Outcome Measure; Pain; Participant; Process; Relaxation; Replacement Arthroplasty; Research; Research Infrastructure; Research Personnel; Risk; Risk Factors; Statistical Data Interpretation; Statistical Models; Subgroup; Technology; Thick; Thinness; Time; United States; Work; aggressive therapy; arthropathies; base; bone; clinical care; cluster computing; cohort; cost; data resource; disability; effective therapy; expectation; genetic information; high risk; knee replacement arthroplasty; longitudinal analysis; new technology; open source; parallel computer; spatiotemporal

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.

抽象的 骨关节炎（OA）是关节炎的最常见形式，也是残疾的常见原因。而OA会影响 仅在美国，数以百万计的人通常是唯一可用的治疗方法 疾病的疼痛和残疾变得太大了。 OA研究和临床护理的进步已经 由于缺乏敏感的生物标志物以及没有分析方法来检测这种情况，极大地阻碍了 一些现有大型数据集中的生物标志物，例如骨关节炎计划（OAI）的数据集。 OAI的磁共振图像（MRI）数据集包含极其有价值的纵向图像数据 从8年内收集的4,000多名受试者。据信软骨损失是 OA中的主导因素，到目前 OAI数据集的。这严重限制了对膝盖软骨变化及其与结果的关系的研究 措施。很难为完整数据集获得基于图像的软骨生物标志物 方法充其量是半自动的。一个关键的挑战是现有方法不会扩展到大型 数据集：财务上（这种分析将耗资数百万美元），也可以从实际的角度来看 - 例如，手动分割软骨可能需要一个人进行十年的全职工作。 该项目的目的是两个方面： 1）我们将发明高级图像分析和统计方法，这将允许真正的大规模 对OAI MRI数据集的分析，即，将使我们能够分析完整的OAI数据集。这些方法会 包括自动细分和表征膝盖软骨并评估差异的方法 主题和跨时间。我们所有的分析软件将以开源形式提供给公众， 免费使用任何人。我们将支持自定义计算簇，云和并行计算。 2）通过促进对整个数据集的大规模分析，拟议的方法将使我们能够重新审视 在先前方法中，由于差距而打开许多重要的临床问题。特别是标准射线照相 OA进展的结果度量（基于Kellgren-Lawence等级和/或关节空间狭窄）具有 低可靠性，难以解释，并且对改变的反应不佳。因此，我们将探索当地软骨 厚度是OA进展的量度及其与OA的假定风险因素的关联， （与期望的对比）仅显示有限，相互冲突或不确定的关联 措施。我们还将研究短期软骨的长期OA进展的预测 特征，这可以帮助识别有快速软骨损失风险最高风险的个体。一旦确定， 然后，这些人可以作为更具侵略性的治疗或临床试验的目标。