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.

项目摘要 解剖结构的形态（或形状）形成了临床医生之间的共同语言，其中， 解剖学上的畸形常常与有害的功能有关。虽然这些观察往往是定性的， 要发现微妙的、定量的形状效应，需要应用数学、统计学和计算 将解剖结构解析为便于检验生物学相关假设的数值表示。 基于粒子的形状建模（PSM）及其相关的软件工具套件，ShapeWorks，使学习 通过在图像分割上自动密集放置同源标志的群体水平形状表示， 具有任意拓扑结构的一般解剖学的说明。ShapeWorks的实用性已在 一系列生物医学应用。尽管它对研究企业有明显的效用， 开源许可证，ShapeWorks没有一个可行的商业化道路，由于固有的权衡 在开发和维护成本以及专业的科学和临床市场之间。ShapeWorks拥有 改变研究人员研究解剖形式的方式的潜力，但其广泛的应用， 医学和生物学的可折叠性受到几个障碍的阻碍， 脸上最重要的障碍是（1）现有形状建模的复杂性和陡峭的学习曲线 流水线及其增加的计算和计算机存储器需求;（2）相当多的专业知识， 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;以及（3）缺乏可互操作的 这些实施方式可以容易地并入生物医学研究实验室。在这个项目中，我们支持- pose ShapeWorksStudio，一个利用ShapeWorks实现自动化人群/患者级别的软件套件 解剖形状的建模，以及Seg 3D-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/半自动分割和交互式手动校正分割 解剖学在目标1中，我们将在一个支持大数据队列的框架中集成ShapeWorks和Seg 3D， 使用户能够以直接的方式透明地从图像数据进行到形状模型。在目标2中， 我们将赋予Seg 3D一种机器学习方法，在统计数据中提供自动分割， 这是一个将图像数据与ShapeWorks提供的特定人群形状先验相结合的cal框架。在Aim中 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 到生物医学研究界常用的编程语言。为了提高可重复性， 我们将开发和传播标准工作流程和特定领域的测试案例。该项目结合了 拥有数十年统计分析和成像经验跨学科研发团队 理解和应用科学家确认，拟议的发展有真实的影响， 生物医学和临床研究社区。我们的长期目标是使ShapeWorks成为标准工具， 形状分析在医学中的应用，本文提出的工作将为实现这一目标奠定基础。