ShapeWorks in the Cloud

云中的 ShapeWorks

• 批准号: 10166337
• 负责人: Shireen Youssef Elhabian
• 金额: $ 21万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10166337
• 关键词: Address; Adherence; Administrative Supplement; Adoption; Anatomy; Applied Research; Architecture; Area; Award; Biological; Biological Sciences; Biology; Cardiology; Clinical; Clinical Data; Clinical Trials; Cloud Computing; Cloud Service; Code; Collection; Communication; Communities; Complex; Complex Analysis; Computer Models; Computer software; Computers; Coupled; Custom; Data; Data Sources; Databases; Disabled Persons; Documentation; Environment; Face; Funding; Goals; Image; Imagery; Language; Learning; Machine Learning; Mathematics; Medical; Medicine; Methods; Modeling; Morphology; Occupations; Online Systems; Orthopedics; Parents; Pathologic; Phenotype; Population; Privatization; Psychology; Readiness; Reproducibility; Research; Research Personnel; Running; Scientist; Services; Shapes; Software Design; Software Engineering; Software Tools; Source Code; Speed; Standardization; System; Techniques; Technology; Testing; Work; base; cohort; computational platform; computerized tools; computing resources; data management; design; experience; flexibility; imaging Segmentation; improved; innovation; large datasets; model development; open data; open source; particle; response; scientific computing; shape analysis; software development; statistics; tool; user-friendly

Project Summary This application is submitted in response to NOT-OD-20-073 as an administrative supplement to the parent award R01AR076120 titled: "Anatomy Directly from Imagery: General-purpose, Scalable, and Open-source Machine Learning Approaches." The form (or shape) of anatomies is the clinical language that describes abnormal mor- phologies tied to pathologic functions. Quantifying such subtle morphological shape changes requires parsing the anatomy into a quantitative description that is consistent across the population in question. For more than 100 years, morphometrics has been an indispensable quantitative tool in medical and biological sciences to study anatomical forms. But its representation capacity is limited to linear distances, angles, and areas. Sta- tistical shape modeling (SSM) is the computational extension of classical morphometric techniques to analyze more detailed representations of complex anatomy and their variability within populations The parent award ad- dresses existing roadblocks for the widespread adoption of SSM computational tools in the context of a flexible and general SSM approach termed particle-based shape modeling (PSM) and its associated suite of open-source software tools, ShapeWorks. ShapeWorks enables learning population-level shape representation via automatic dense placement of homologous landmarks on image segmentations of general anatomy with arbitrary topology. The utility of ShapeWorks has been demonstrated in a range of biomedical applications. ShapeWorks has the potential to transform the way researchers approach studies of anatomical forms, but its widespread applicability and impact to medicine and biology are hindered by computational barriers that most existing shape modeling packages face. The goal of this supplement award is to provide supplemental support for Aim 3 of the parent award to leverage best practices in software development and advances in cloud computing to enable researchers with limited computational resources and/or large-scale cohorts to build and execute custom SSM workflows us- ing remote scalable computational resources. To achieve this goal, we have developed a plan to enhance the design, implementation, and cloud-readiness of ShapeWorks and augmented our scientific team to add senior, experienced software engineers/developers who have extensive experience in professional programming, code refactoring, and scientific computing. This award will provide our team with the support necessary to (Aim 1) de- sign ShapeWorks as a collection of modular and reusable services, (Aim 2) decouple ShapeWorks services from explicitly encoded data sources, and (Aim 3) refactor ShapeWorks to scale efficiently on the cloud. All software development will be performed in adherence to software engineering practices and design principles, including coding style, documentation, and version control. The proposed efforts will be released as open-source software in a manner consistent with the principles of reproducible research and the practices of open science. Our long- term goal is to make ShapeWorks a standard tool for shape analyses in medicine, and the work proposed herein in addition to the parent award will establish the groundwork for achieving this goal.

项目摘要 本申请是作为对原裁决的行政补充，根据NOT-OD-20-073提交的 R 01 AR 076120标题：“直接从图像中解剖：通用、可扩展和开源机器 学习方法。“解剖结构的形式（或形状）是描述异常莫尔- 与病理功能相关的生理学量化这种细微的形态形状变化需要解析 将解剖学转化为在所讨论的人群中一致的定量描述。以上 100年来，形态计量学一直是医学和生物科学中不可或缺的定量工具， 研究解剖学形式。但其表示能力仅限于线性距离、角度和区域。Sta- 统计形状建模（SSM）是经典形态测量技术的计算扩展， 更详细地展示了复杂的解剖结构及其在人群中的变异性 为SSM计算工具的广泛采用，在灵活的背景下， 和一般的SSM方法称为基于粒子的形状建模（PSM）及其相关套件的开源 软件工具，ShapeWorks。ShapeWorks支持通过自动学习人口级别的形状表示 在具有任意拓扑结构的一般解剖结构的图像分割上密集放置同源界标。 ShapeWorks的实用性已在一系列生物医学应用中得到证明。ShapeWorks具有 有可能改变研究人员研究解剖形式的方式，但其广泛的适用性 而现有的形状建模技术中， 包装面对。该补充奖的目标是为母公司的目标3提供补充支持 该奖项旨在利用软件开发的最佳实践和云计算的进步， 利用有限的计算资源和/或大规模的队列来构建和执行定制的SSM工作，这向我们展示了- 远程可伸缩计算资源。为了达到这个目标，我们制定了一项计划， 设计、实施和云就绪，并扩充了我们的科学团队， 经验丰富的软件工程师/开发人员，在专业编程，代码 重构和科学计算。该奖项将为我们的团队提供必要的支持（目标1）去- 将ShapeWorks标记为模块化和可重用服务集合，（目标2）将ShapeWorks服务与 显式编码的数据源，以及（目标3）重构ShapeWorks以在云上有效扩展。所有软件 开发将遵循软件工程实践和设计原则，包括 编码风格、文档和版本控制。这些提议的努力将作为开源软件发布 以符合可重复研究原则和开放科学实践的方式。我们长久以来- 我们的目标是使ShapeWorks成为医学中形状分析的标准工具，本文提出的工作 除了家长奖之外，还将为实现这一目标奠定基础。