Anatomy Directly from Imagery: General-purpose, Scalable, and Open-source Machine Learning Approaches

直接从图像进行解剖：通用、可扩展和开源机器学习方法

• 批准号: 9803774
• 负责人: Shireen Youssef Elhabian
• 金额: $ 63.18万
• 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 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9803774
• 关键词: Address; Adoption; Age; Algorithms; Anatomic Models; Anatomic Surface; Anatomy; Area; Biological; Biological Process; Biological Sciences; Biological Testing; Biology; Biomedical Research; Brain; Bypass; Cardiology; Cessation of life; Clinical; Clinical Data; Cloud Computing; Collection; Communities; Complex; Complex Analysis; Computational Technique; Computer Simulation; Computer software; Computers; Custom; Data; Data Set; Databases; Development; Disease; Felis catus; Funding; Generations; Geometry; Goals; Human; Ice; Image; Imagery; Injury; Intuition; Laboratory Research; Learning; Machine Learning; Magnetic Resonance Imaging; Mathematical Computing; Measures; Medical; Medicine; Mission; Modeling; Modernization; Modification; Morphogenesis; National Institute of General Medical Sciences; Occupations; Online Systems; Organism; Orthopedics; Pathologic; Population; Reproducibility; Research; Research Personnel; Resolution; Science; Scientist; Shapes; Site; Software Engineering; Software Tools; Specialist; Speed; Statistical Data Interpretation; Structure; Supervision; Surface; Techniques; Technology; Time; Training; Variant; Visual; Visualization software; Work; base; biomedical resource; clinical care; clinical investigation; clinically relevant; cohort; computerized tools; computing resources; deep learning; experience; flexibility; global health; graphical user interface; image archival system; image processing; imaging Segmentation; in vivo imaging; innovation; machine learning algorithm; model development; multidisciplinary; open source; particle; preference; software development; tool; usability; user-friendly

Project Summary The form (or shape) and function relationship of anatomical structures is a central theme in biology where abnor- mal shape changes are closely tied to pathological functions. Morphometrics has been an indispensable quan- titative tool in medical and biological sciences to study anatomical forms for more than 100 years. Recently, the increased availability of high-resolution in-vivo images of anatomy has led to the development of a new generation of morphometric approaches, called statistical shape modeling (SSM), that take advantage of modern computa- tional techniques to model anatomical shapes and their variability within populations with unprecedented detail. SSM stands to revolutionize morphometric analysis, but its widespread adoption is hindered by a number of sig- nificant challenges, including the complexity of the approaches and their increased computational requirements, relative to traditional morphometrics. Arguably, however, the most important roadblock to more widespread adop- tion is the lack of user-friendly and scalable software tools for a variety of anatomical surfaces that can be readily incorporated into biomedical research labs. The goal of this proposal is thus to address these challenges in the context of a flexible and general SSM approach termed particle-based shape modeling (PSM), which automat- ically constructs optimal statistical landmark-based shape models of ensembles of anatomical shapes without relying on any specific surface parameterization. The proposed research will provide an automated, general- purpose, and scalable computational solution for constructing shape models of general anatomy. In Aim 1, we will build computational and machine learning algorithms to model anatomies with complex surface topologies (e.g., surface openings and shared boundaries) and highly variable anatomical populations. In Aim 2, we will introduce an end-to-end machine learning approach to extract statistical shape representation directly from im- ages, requiring no parameter tuning, image pre-processing, or user assistance. In Aim 3, we will provide intuitive graphical user interfaces and visualization tools to incorporate user-defined modeling preferences and promote the visual interpretation of shape models. We will also make use of recent advances in cloud computing to enable researchers with limited computational resources and/or large cohorts to build and execute custom SSM work- flows using remote scalable computational resources. Algorithmic developments will be thoroughly evaluated and validated using existing, fully funded, large-scale, and constantly growing databases of CT and MRI images lo- cated on-site. Furthermore, we will develop and disseminate standard workflows and domain-specific use cases for complex anatomies to promote reproducibility. Efforts to develop the proposed technology are aligned with the mission of the National Institute of General Medical Sciences (NIGMS), and its third strategic goal: to bridge biology and quantitative science for better global health through supporting the development of and access to computational research tools for biomedical research. Our long-term goal is to increase the clinical utility and widespread adoption of SSM, and the proposed research will establish the groundwork for achieving this goal.

项目摘要 解剖结构的形式（或形状）和功能关系是生物学的中心主题 Mal形状的变化与病理功能紧密相关。形态计量学一直是必不可少的 医学和生物学科学的名义工具研究解剖形式已有100多年的历史。最近， 增加解剖学的高分辨率体内图像的可用性已导致新一代的发展 形态计量学方法，称为统计形状建模（SSM），利用现代计算 建模解剖学形状及其在人群中具有前所未有的细节的变异性。 SSM代表彻底改变形态分析，但其宽度采用的采用受到许多sig-的阻碍 挑战，包括方法的复杂性及其增加的计算要求， 相对于传统的形态计量学。但是，可以说，最重要的障碍是采用更广泛的障碍 - 缺乏用于各种解剖表面的用户友好且可扩展的软件工具 纳入生物医学研究实验室。因此，该提案的目的是解决这些挑战 灵活和一般SSM方法的上下文称为基于粒子的形状建模（PSM），该方法自动 ifully构建了没有解剖形状的合奏的最佳统计里程碑形状模型 依靠任何特定的表面参数化。拟议的研究将提供自动化的一般性 - 目的和可扩展的计算解决方案，用于构建一般解剖结构的形状模型。在AIM 1中，我们 将构建计算和机器学习算法，以建模具有复杂表面拓扑的解剖学 （例如，表面开口和共享边界）和高度可变的解剖群体。在AIM 2中，我们将 引入端到端的机器学习方法，以直接从IM-提取统计形状表示 年龄，不需要参数调整，图像预处理或用户帮助。在AIM 3中，我们将提供直观的 图形用户界面和可视化工具，以合并用户定义的建模偏好并促进 形状模型的视觉解释。我们还将利用云计算的最新进展来启用 具有有限计算资源和/或大型队列的研究人员建立和执行自定义SSM工作 - 使用远程可扩展计算资源的流量。算法开发将得到彻底评估，并 使用现有的，完全资助的大规模且不断增长的CT和MRI图像数据库进行验证 现场。此外，我们将开发和传播标准工作流和域特异性用例 复杂的解剖学促进繁殖。开发拟议技术的努力与 美国国家一般医学科学研究所（NIGMS）的使命及其第三个战略目标：桥梁 生物学和定量科学通过支持并获得的发展，以改善全球健康 生物医学研究的计算研究工具。我们的长期目标是增加临床实用性和 SSM的宽度采用，拟议的研究将为实现这一目标建立基础。