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中的一个中心主题， 畸形的形态变化与病理功能密切相关。形态计量学已成为一个不可或缺的方法， 100多年来，医学和生物科学中研究解剖学形式的虚拟工具。近日 高分辨率体内解剖图像的增加的可用性已经导致了新一代 形态测量方法，称为统计形状建模（SSM），利用现代计算， 传统的技术，以前所未有的细节建模解剖形状及其在人群中的变异性。 SSM将彻底改变形态学分析，但其广泛采用受到许多信号的阻碍， 巨大的挑战，包括方法的复杂性及其增加的计算要求， 相对于传统的形态学。然而，可以说，更广泛的adop最重要的障碍- 问题是缺乏用户友好和可扩展的软件工具，用于各种解剖表面，可以很容易地 纳入生物医学研究实验室。因此，本提案的目标是在2010年应对这些挑战。 上下文的一个灵活的和一般的SSM方法称为基于粒子的形状建模（PSM），自动化， ically构造解剖形状的集合的最佳基于统计标志的形状模型， 依赖于任何特定的表面参数化。这项研究将提供一个自动化的，通用的- 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。目标1： 将建立计算和机器学习算法，以模拟具有复杂表面拓扑结构的解剖结构 (e.g.,表面开口和共享边界）和高度可变的解剖群体。在目标2中，我们将 介绍了一种端到端机器学习方法来直接从IM中提取统计形状表示， 无需参数调整、图像预处理或用户协助。在目标3中，我们将提供直观的 图形用户界面和可视化工具，以纳入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， 有限的计算资源和/或大量的研究人员来构建和执行自定义SSM工作- 使用远程可扩展计算资源的流程。将彻底评估军事发展， 使用现有的、资金充足的、大规模的、不断增长的CT和MRI图像数据库进行验证， 现场拍摄此外，我们将开发和传播标准工作流程和特定领域的用例 用于复杂的解剖结构，以提高可重复性。开发拟议技术的努力与 国家普通医学科学研究所（NIGMS）的使命及其第三个战略目标： 生物学和定量科学，通过支持发展和获取 生物医学研究的计算研究工具。我们的长期目标是提高临床实用性， SSM的广泛采用，拟议的研究将为实现这一目标奠定基础。