ShapeWorksStudio: An Integrative, User-Friendly, and Scalable Suite for Shape Representation and Analysis

ShapeWorksStudio：用于形状表示和分析的集成、用户友好且可扩展的套件

• 批准号: 10023935
• 负责人: Shireen Youssef Elhabian
• 金额: $ 25.66万
• 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: 2019-09-30 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10023935
• 关键词: Address; Adoption; Anatomic Models; Anatomy; Applied Research; Area; Big Data; Binding; Biological; Biological Sciences; Biological Testing; Biology; Biomedical Research; Cardiology; Clinical; Clinical Research; Clinical Trials; Communities; Complex; Complex Analysis; Computer software; Computers; Consensus; Data; Data Set; Development; Dimensions; Electronic Mail; Ensure; Exhibits; Face; Funding; Future; Goals; Image; Interdisciplinary Study; Laboratory Research; Language; Learning; Licensing; Machine Learning; Maintenance; Manuals; Mathematics; Measures; Medical; Medicine; Memory; Methods; Modeling; Modernization; Modification; Morphology; Normalcy; Operative Surgical Procedures; Orthopedics; Phenotype; Population; Process; Programming Languages; Psychology; Reconstructive Surgical Procedures; Reproducibility; Research; Research Personnel; Scientist; Shapes; Software Engineering; Software Tools; Statistical Data Interpretation; Supervision; Techniques; Technology; Testing; Time; Work; automated segmentation; base; clinical application; clinical care; clinical investigation; cohort; commercialization; computerized tools; cost; design; experience; flexibility; imaging Segmentation; improved; innovation; interest; interoperability; medical implant; open source; outreach; particle; patient population; reconstruction; research and development; shape analysis; software development; statistics; tool; usability; user-friendly

Project Summary The morphology (or shape) of anatomical structures forms the common language among clinicians, where ab- normalities in anatomical shapes are often tied to deleterious function. While these observations are often quali- tative, finding subtle, quantitative shape effects requires the application of mathematics, statistics, and computing to parse the anatomy into a numerical representation that will facilitate testing of biologically relevant hypotheses. Particle-based shape modeling (PSM) and its associated suite of software tools, ShapeWorks, enable learning population-level shape representation via automatic dense placement of homologous landmarks on image seg- mentations of general anatomy with arbitrary topology. The utility of ShapeWorks has been demonstrated in a range of biomedical applications. Despite its obvious utility for the research enterprise and highly permissive open-source license, ShapeWorks does not have a viable commercialization path due to the inherent trade-off between development and maintenance costs, and a specialized scientific and clinical market. ShapeWorks has the potential to transform the way researchers approach studies of anatomical forms, but its widespread ap- plicability to medicine and biology is hindered by several barriers that most existing shape modeling packages face. The most important roadblocks are (1) the complexity and steep learning curve of existing shape modeling pipelines and their increased computational and computer memory requirements; (2) the considerable expertise, time, and effort required to segment anatomies of interest for statistical analyses; and (3) the lack of interoperable implementations that can be readily incorporated into biomedical research laboratories. In this project, we pro- pose ShapeWorksStudio, a software suite that leverages ShapeWorks for the automated population-/patient-level modeling of anatomical shapes, and Seg3D – a widely used open-source tool to visualize and process volumet- ric images – for flexible manual/semiautomatic segmentation and interactive manual correction of segmented anatomy. In Aim 1, we will integrate ShapeWorks and Seg3D in a framework that supports big data cohorts to enable users to transparently proceed from image data to shape models in a straightforward manner. In Aim 2, we will endow Seg3D with a machine learning approach that provides automated segmentations within a statisti- cal framework that combines image data with population-specific shape priors provided by ShapeWorks. In Aim 3, we will support interoperability with existing open-source software packages and toolkits, and provide bindings to commonly used programming languages in the biomedical research community. To promote reproducibility, we will develop and disseminate standard workflows and domain-specific test cases. This project combines an interdisciplinary research and development team with decades of experience in statistical analysis and image understanding, and application scientists to confirm that the proposed developments have a real impact on the biomedical and clinical research communities. Our long-term goal is to make ShapeWorks a standard tool for shape analyses in medicine, and the work proposed herein will establish the groundwork for achieving this goal.

项目概要 解剖结构的形态（或形状）形成了信徒之间的共同语言，其中 ab- 解剖形状的正常性通常与有害的功能有关，而这些观察结果通常是合格的。 假设，寻找微妙的、定量的形状效应需要应用数学、统计学和计算 将解剖结构解析为数字表示，这将有助于测试生物学相关的假设。 基于粒子的形状建模 (PSM) 及其相关软件工具套件 ShapeWorks 支持学习 通过在图像片段上自动密集放置同源地标来实现群体级别的形状表示 ShapeWorks 的实用性已在 尽管它对研究企业有明显的实用性并且高度许可。 开源许可证，由于固有的权衡，ShapeWorks 没有可行的商业化路径 开发和维护成本之间的平衡，以及专门的科学和临床市场。 其具有改变研究人员进行解剖形式研究的方式的潜力，但其广泛应用 医学和生物学的适用性受到大多数现有形状建模包的几个障碍的阻碍 最重要的障碍是（1）现有形状建模的复杂性和学习曲线陡峭。 (2) 丰富的专业知识， 分割感兴趣的解剖结构以进行统计分析所需的时间和精力；(3) 缺乏可互操作性； 在这个项目中，我们支持可以轻松纳入生物医学研究实验室的实施。 ShapeWorksStudio，一个利用 ShapeWorks 实现人群/患者级别自动化的软件套件 解剖形状建模和 Seg3D（一种广泛使用的开源工具，用于可视化和处理体积） ric 图像 – 用于灵活的手动/半自动分割和分段的交互式手动校正 在目标 1 中，我们将把 ShapeWorks 和 Seg3D 集成到一个支持大数据队列的框架中。 使用户能够以简单的方式透明地从图像数据转换为形状模型。 我们将赋予 Seg3D 一种机器学习方法，该方法可以在统计数据中提供自动分割 cal 框架，将图像数据与 ShapeWorks 提供的特定人群形状先验相结合。 3、我们将支持与现有开源软件包和工具包的互操作性，并提供绑定 生物医学研究界常用的编程语言，以提高可重复性， 我们将开发和传播标准工作流程和特定领域的测试用例。 拥有数十年统计分析和图像经验的跨学科研发团队 理解和应用科学家确认所提出的发展对 我们的长期目标是使 ShapeWorks 成为生物医学和临床研究社区的标准工具。 医学中的形状分析，本文提出的工作将为实现这一目标奠定基础。