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

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。 MAL的形态变化与病理功能密切相关。形态计量学已成为一门不可或缺的学科-- 100多年来，医学和生物科学中研究解剖形态的有力工具。最近， 高分辨率的活体解剖图像的可用性增加导致了新一代的发展 形态测量方法，称为统计形状建模(SSM)，它利用现代计算- 传统技术，以前所未有的细节模拟种群内的解剖形状及其变异性。 SSM有望给形态计量学分析带来革命性的变化，但它的广泛应用受到了许多符号分析的阻碍。 Nifi无法挑战，包括方法的复杂性及其增加的计算要求， 相对于传统的形态测量学。然而，可以说，ADOP更广泛的最重要障碍是- 功能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 整合到生物医学研究实验室。因此，这项提案的目标是在 一种称为基于粒子的形状建模的fl可扩展和通用的形状建模方法的背景，它自动地- 构建基于统计里程碑的解剖形状集成的最佳形状模型，而不需要 依赖于任何特定的fic曲面参数化。拟议的研究将提供一个自动化的、通用的- 用于构建普通解剖学形状模型的可扩展计算解决方案。在目标1中，我们 我将构建计算和机器学习算法来模拟具有复杂表面拓扑的解剖结构 (例如，表面开口和共享边界)和高度可变的解剖种群。在目标2中，我们将 介绍了一种端到端的机器学习方法，用于直接从图像中提取统计形状表示。 无需参数调整、图像预处理或用户帮助。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，可结合用户特定的fiNED建模首选项并促进 形状模型的视觉解释。我们还将利用云计算的最新进展来实现 具有有限计算资源和/或大量团队来构建和执行定制SSM工作的研究人员- 使用远程可扩展计算资源的fl操作系统。将对算法开发进行彻底评估并 使用现有、资金充足、大规模且不断增长的CT和MRI图像日志数据库进行验证- 现场引证。此外，我们将开发和发布标准工作flOWS和领域特定的fic++用例。 用于复杂的解剖以促进重复性。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：在 生物学和量化科学，通过支持发展和获得更好的全球健康 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 广泛采用SSM，拟议的研究将为实现这一目标奠定基础。

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.