Deep Learning for Characterizing Knee Joint Degeneration Predicting Progression of Osteoarthritis and Total Knee Replacement

摘要 该提议旨在开发深度学习方法来自动提取形态图像 与膝骨性关节炎(OA)和全膝关节置换术相关的特征。同时，量化评价 磁共振成像(MRI)在临床环境下的OA研究中发挥着核心作用，MR经常报告 往往是主观的、定性的，并且没有使用流行病学研究中使用的分级方案 因为它们非常耗时，不能满足当今的需求 不断变化的医疗保健情景。我们面临的大数据挑战和机遇使我们有必要建设 使工具(I)能够自动提取形态的OA成像特征，目的是评估 在以前从未探索过的更大样本量上预测疾病进展的能力； 通过应用状态发现未开发的数据驱动的成像特征来发现潜在模式 ART深度学习方法(1)；(3)将多模式成像与临床、功能、活动和 其他数据，以确定关节退变在骨关节炎的轨迹。利用这些最先进的技术的力量 技术，以及大量带注释的图像数据集的非凡可用性；在这个项目中，我们 建议开发一种自动后处理流水线，能够分割肌肉骨骼组织和 识别磁共振成像(MRI)中的形态OA特征，如常用MRI所定义的 分级系统。关节组织形态分级的自动化将是一个重要的 在骨性关节炎研究和临床实践方面都取得了突破。它将使大样本的分析成为可能， 协助放射科医生/临床医生对图像进行分级，花费相对较短的时间，降低成本，以及 可能会潜在地改进分类模型。用于识别的自动管道的可用性 MRI中的形态异常将极大地改变临床实践，包括半定量 分级，而不是在放射学临床报告中的主观印象。在这项研究中，我们还旨在开发一种 一种完全有监督的深度学习方法，用于获取数据驱动的非线性和语义表示 能够利用隐藏在3D复杂性中的潜在信息的基本特征之间的聚合 MR图像，消除了对所选特征的名义等级的需要。这第二个目标，当处于高潮时 风险也有潜在的异常高的影响；因为它背离了经典的假设驱动的研究，以及 建立了一个新的翻译平台，以革命性地对研究中的磁共振图像进行形态分级， 而且是范式转换，因为它可以为例程提供更定量的特征驱动的基础 放射学临床报告。这项提议的临床影响在于第三个目标(R33阶段)，在这个阶段，我们 建议将R61阶段开发的解决方案转换为加州大学旧金山分校临床档案中的图像 (PAC)，并计划将人口统计和临床数据也纳入电子健康记录，以建立 定义全膝关节置换的模型。

ABSTRACT This proposal aims to develop deep learning methods to automate the extraction of morphological imaging features relevant to knee osteoarthritis (OA), and total knee replacement. While, quantitative evaluation Magnetic Resonance Imaging (MRI) plays a central role in OA research in the clinical setting MR reports often tend to be subjective, qualitative, and the grading schemes utilized in epidemiological research are not used because they are extraordinarily time consuming and do not lend themselves to the demands of todays changing healthcare scenario. The “Big Data” challenge and opportunity facing us makes it necessary to build enabling tools (i) to automate the extraction of morphological OA imaging features, with the aim of evaluating disease progression prediction capabilities on larger sample sizes that have never been explored before; (ii) to discover latent patterns by uncovering unexplored data-driven imaging features by the application of state of the art deep learning approaches (1); (iii) combine multi-modality imaging with clinical, functional, activity, and other data to define the trajectory of joint degeneration in OA. Leveraging the power of these state of the art techniques, and with the extraordinary availability of a large datasets of annotated images; in this project, we propose to develop an automatic post-processing pipeline able to segment musculoskeletal tissues and identify morphological OA features in Magnetic Resonance Images (MRI), as defined by commonly used MRI grading systems. Automation of morphological grading of the tissues in the joint would be a significant breakthrough in both OA research and clinical practice. It would enable the analysis of large sample sizes, assist the radiologist/clinician in the grading of images, take a relatively short amount of time, reduce cost, and could potentially, improve classification models. The availability of automatic pipelines for the identification of morphological abnormities in MRI would drastically change clinical practice, and include semi-quantitative grades, rather than subjective impressions in radiology clinical reports. In this study, we also aim to develop a complete supervised deep learning approach to obtain data-driven representations as non-linear and semantic aggregation among elementary features able to exploit the latent information hidden in the complexity of a 3D MR images, eliminating the need for nominal grades of selected features. This second aim, while being at high risk has also a potential exceptional high impact; as it departs from the classical hypothesis driven studies, and builds a novel translational platform to revolutionize morphological grading of MR images in research studies, but also is paradigm-shifting in that it may provide a more quantitative feature driven basis for routine radiological clinical reports. The clinical impact of this proposal lies in the third aim (R33 phase), in which we propose to translate the solutions developed in the R61 phase on images in the UCSF clinical archives (PACS), and plan to include also demographic and clinical data in the electronic health records, to build the models defining total knee replacements.