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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

基本信息

批准号：
10171789
负责人：
Shireen Youssef Elhabian
金额：
$ 61.44万
依托单位：
UNIVERSITY OF UTAH
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-01 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10171789
关键词：
Address Adoption 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 Models Computer software Computers Custom Data Databases Development Disease Funding Generations Geometry Goals Human Image Imagery Injury Intuition Laboratory Research Learning Machine Learning Magnetic Resonance Imaging Mathematics 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 algorithm development anatomic imaging 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 improved in vivo imaging innovation large datasets machine learning algorithm multidisciplinary open source particle preference software development tool usability user-friendly

项目摘要

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.

项目总结

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

An Image-based Approach for 3D Left Atrium Functional Measurements.

DOI：
10.22489/cinc.2020.459
发表时间：
2020-09
期刊：
Computing in cardiology
影响因子：
0
作者：
Morris A;Kholmovski E;Marrouche N;Cates J;Elhabian S
通讯作者：
Elhabian S

dpVAEs: Fixing Sample Generation for Regularized VAEs.

DOI：
10.1007/978-3-030-69538-5_39
发表时间：
2020-11
期刊：
Computer vision - ACCV ... : ... Asian Conference on Computer Vision : proceedings. Asian Conference on Computer Vision
影响因子：
0
作者：
Bhalodia R;Lee I;Elhabian S
通讯作者：
Elhabian S

Statistical multi-level shape models for scalable modeling of multi-organ anatomies.

DOI：
10.3389/fbioe.2023.1089113
发表时间：
2023
期刊：
Frontiers in bioengineering and biotechnology
影响因子：
5.7
作者：
通讯作者：

Multi-level multi-domain statistical shape model of the subtalar, talonavicular, and calcaneocuboid joints.

多层多域统计形状模型，塔龙腔和钙尼角关节。

DOI：
10.3389/fbioe.2022.1056536
发表时间：
2022
期刊：
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
影响因子：
5.7
作者：
Peterson, Andrew C.;Lisonbee, Rich J.;Krahenbuhl, Nicola;Saltzman, Charles L.;Barg, Alexej;Khan, Nawazish;Elhabian, Shireen Y.;Lenz, Amy L.
通讯作者：
Lenz, Amy L.

Particle-Based Shape Modeling for Arbitrary Regions-of-Interest.

适用于任意感兴趣区域的基于粒子的形状建模。