CGV: Medium: Collaborative Research: Developing conceptual models for navigation, marking, and inspection in the context of 3D image segmentation

CGV:媒介:协作研究:开发 3D 图像分割背景下的导航、标记和检查概念模型

基本信息

  • 批准号:
    1302142
  • 负责人:
  • 金额:
    $ 28.63万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2013
  • 资助国家:
    美国
  • 起止时间:
    2013-06-15 至 2018-05-31
  • 项目状态:
    已结题

项目摘要

3D image segmentation is an important and ubiquitous task in image-oriented scientific disciplines, particularly biomedicine, where images provide the basis for biological discovery. While imaging techniques reveal spatial content and activities within an entire subject, ultimately biologists are interested in specific anatomical structures (e.g., organs, tissues, cells, etc.). Delineation of the structures of interest within a given set of images is therefore a typical first-step in the data-to-knowledge pipeline, with both the efficiency and accuracy of segmentation critically affecting how the data is utilized in research and clinical practice. Creating accurate segmentations, particularly for 3D biomedical images, is a non-trivial task that calls for cooperation between humans and computers. While human experts, with their superior visual perception skills and vast knowledge and experience acquired from years of training, ultimately decide what constitutes an accurate segmentation, they lack the objectivity or efficiency of computational algorithms. On the other hand, without expert guidance, segmentation algorithms easily fail in the presence of the noise and ambiguity that are inevitable in biomedical images. In this research the PIs will investigate 3D image segmentation as a human-computer interaction paradigm to better understand the human factors that are involved in the current segmentation process, with the goal of making the process more efficient, accurate and repeatable. The team's hypothesis is that the segmentation process could be significantly improved through a deeper understanding of how people perform low-level perception and cognition tasks in the context of 3D segmentation (e.g., visual cues, delineation of structures by marks, and local accuracy or quality criteria), and how domain experts wish to specify high-level segmentation constraints (e.g., connectivity, topology, and shape). To test this hypothesis the PIs will analyze the segmentation process by domain experts that span a reasonable subspace of the actual segmentors and segmentation tasks in biology and clinical practice, to define a conceptual framework that captures the low-level perception and cognitive elements of segmentation as well as the higher-level information related to navigation, marking, and inspection. Building upon and instantiating the framework, the team will work with experts to develop a prototype segmentation tool that explores novel interaction and visualization paradigms as well as their supporting algorithms. The prototype tool will be used to both verify the conceptual framework and to create a more effective practical solution to segmentation.Broader Impacts: By formulating and studying segmentation as a human perception and cognitive task, this work represents a major departure from existing research on either segmentation algorithms or tools. The resulting conceptual framework will serve as a bridge between the two communities, leading both to better designs for current and future segmentation tools and the framing of new problems for segmentation algorithms. For end users, the working prototype will support a more effective segmentation experience that is powered by the underlying conceptual framework. Furthermore, formalizing the kinds of perceptual cues and conceptual models users have when approaching the segmentation problem will serve as a useful test case for understanding the more general question of how perception and cognition interact when they are re-mapped to solve a problem they were never designed for. To disseminate the findings of this research, the PIs will release their working prototype as an open-source project, which can then serve as a shared communication platform between algorithm developers, tool developers, and end users.
三维图像分割是以图像为导向的科学领域中一项重要且普遍存在的任务,特别是在生物医学领域,图像是生物发现的基础。虽然成像技术揭示了整个对象的空间内容和活动,但生物学家最终对特定的解剖结构(如器官、组织、细胞等)感兴趣。因此,在一组给定的图像中描绘感兴趣的结构是数据到知识管道中典型的第一步,分割的效率和准确性都至关重要地影响着数据在研究和临床实践中的利用方式。创建准确的分割,特别是3D生物医学图像,是一项需要人类和计算机合作的艰巨任务。虽然人类专家凭借其出众的视觉感知技能以及从多年培训中获得的丰富知识和经验,最终决定了什么构成准确的分割,但他们缺乏计算算法的客观性或效率。另一方面,在没有专家指导的情况下,分割算法很容易在生物医学图像中不可避免的噪声和歧义的存在下失败。在这项研究中,PI将研究3D图像分割作为一种人机交互范式,以更好地理解当前分割过程中涉及的人为因素,目标是使该过程更高效、更准确和更具可重复性。该团队的假设是,通过更深入地了解人们如何在3D分割环境中执行低级感知和认知任务(例如,视觉线索、通过标记描绘结构以及局部准确性或质量标准),以及领域专家希望如何指定高级分割约束(例如,连通性、拓扑和形状),分割过程可以得到显著改善。为了验证这一假设,PI将分析领域专家的分割过程,这些分割过程跨越生物学和临床实践中实际分割者和分割任务的合理子空间,以定义一个概念框架,该框架捕捉分割的低级别感知和认知元素以及与导航、标记和检查相关的高级信息。在该框架的基础上并实例化,该团队将与专家合作开发一个原型分割工具,该工具探索新的交互和可视化范例及其支持算法。原型工具将用于验证概念框架和创建更有效的实际分割解决方案。广泛的影响:通过将分割作为人类感知和认知任务来制定和研究,这项工作与现有的分割算法或工具研究大相径庭。由此产生的概念框架将成为两个社区之间的桥梁,导致双方更好地设计当前和未来的分割工具,并为分割算法制定新的问题框架。对于最终用户,工作原型将支持由底层概念框架支持的更有效的细分体验。此外,将用户在处理分割问题时拥有的感知线索和概念模型的种类形式化,将作为一个有用的测试用例,以理解更一般的问题,即当感知和认知被重新映射以解决它们从未设计过的问题时,它们如何相互作用。为了传播这项研究的结果,PI将以开源项目的形式发布他们的工作原型,然后可以作为算法开发人员、工具开发人员和最终用户之间的共享交流平台。

项目成果

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Cindy Grimm其他文献

Machine vision-based assessment of fall color changes in apple leaves and its relationship with nitrogen concentration
基于机器视觉的苹果叶片秋季颜色变化评估及其与氮浓度的关系
  • DOI:
    10.1016/j.compag.2025.110366
  • 发表时间:
    2025-09-01
  • 期刊:
  • 影响因子:
    8.900
  • 作者:
    Achyut Paudel;Jostan Brown;Priyanka Upadhyaya;Atif Bilal Asad;Safal Kshetri;Joseph R. Davidson;Cindy Grimm;Ashley Thompson;Bernardita Sallato;Matthew D. Whiting;Manoj Karkee
  • 通讯作者:
    Manoj Karkee
Perceptually meaningful image editing Manipulating perceived depth and creating the illusion of motion in 2 D images
具有感知意义的图像编辑 操纵感知深度并在 2D 图像中创建运动错觉
  • DOI:
  • 发表时间:
    2007
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Reynold J. Bailey;Cindy Grimm
  • 通讯作者:
    Cindy Grimm
Tree detection and in-row localization for autonomous precision orchard management
  • DOI:
    10.1016/j.compag.2024.109454
  • 发表时间:
    2024-12-01
  • 期刊:
  • 影响因子:
  • 作者:
    Jostan Brown;Achyut Paudel;Deven Biehler;Ashley Thompson;Manoj Karkee;Cindy Grimm;Joseph R. Davidson
  • 通讯作者:
    Joseph R. Davidson
Sketching reaction-diffusion texture
素描反应扩散纹理
  • DOI:
    10.2312/sbm/sbm06/107-114
  • 发表时间:
    2006
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Ly Phan;Cindy Grimm
  • 通讯作者:
    Cindy Grimm
Manipulating perceived depth and creating the illusion of motion in 2D images
操纵感知深度并在 2D 图像中创建运动错觉
  • DOI:
  • 发表时间:
    2007
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Reynold J. Bailey;Cindy Grimm
  • 通讯作者:
    Cindy Grimm

Cindy Grimm的其他文献

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{{ truncateString('Cindy Grimm', 18)}}的其他基金

Collaborative Research: NRI: FND: Grounded Reasoning about Robot Capabilities for Law and Policy
合作研究:NRI:FND:关于机器人法律和政策能力的基础推理
  • 批准号:
    2024872
  • 财政年份:
    2020
  • 资助金额:
    $ 28.63万
  • 项目类别:
    Standard Grant
CCRI: Medium: Collaborative Research: Physical Robotic Manipulation Test Facility
CCRI:媒介:协作研究:物理机器人操作测试设施
  • 批准号:
    1925715
  • 财政年份:
    2019
  • 资助金额:
    $ 28.63万
  • 项目类别:
    Standard Grant
RI:Small: Leveraging Human Manipulation Skills to Advance Near Contact Robotic Grasping and In-Hand Stabilization
RI:Small:利用人类操纵技能推进近距离接触机器人抓取和手持稳定性
  • 批准号:
    1911050
  • 财政年份:
    2019
  • 资助金额:
    $ 28.63万
  • 项目类别:
    Standard Grant
CI-P: Physical robotic manipulation test facility
CI-P:物理机器人操纵测试设施
  • 批准号:
    1730126
  • 财政年份:
    2017
  • 资助金额:
    $ 28.63万
  • 项目类别:
    Standard Grant
REU Site: Robots in the Real World
REU 网站:现实世界中的机器人
  • 批准号:
    1659746
  • 财政年份:
    2017
  • 资助金额:
    $ 28.63万
  • 项目类别:
    Standard Grant
Collaborative Research: Biological Shape Spaces, Transforming Shape into Knowledge
合作研究:生物形状空间,将形状转化为知识
  • 批准号:
    1313810
  • 财政年份:
    2012
  • 资助金额:
    $ 28.63万
  • 项目类别:
    Standard Grant
Collaborative Research: Biological Shape Spaces, Transforming Shape into Knowledge
合作研究:生物形状空间,将形状转化为知识
  • 批准号:
    1053171
  • 财政年份:
    2010
  • 资助金额:
    $ 28.63万
  • 项目类别:
    Standard Grant
CPATH T: Active Learning for Transformation of the Undergraduate Experience
CPATH T:主动学习促进本科生体验的转变
  • 批准号:
    0722328
  • 财政年份:
    2007
  • 资助金额:
    $ 28.63万
  • 项目类别:
    Standard Grant
Surface Construction and Comparison using Manifolds
使用流形的表面构造和比较
  • 批准号:
    0429856
  • 财政年份:
    2004
  • 资助金额:
    $ 28.63万
  • 项目类别:
    Continuing Grant
CAREER: A Composition System for Computer Graphics
职业:计算机图形合成系统
  • 批准号:
    0238062
  • 财政年份:
    2003
  • 资助金额:
    $ 28.63万
  • 项目类别:
    Continuing Grant

相似海外基金

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CGV:媒介:协作研究:开发 3D 图像分割背景下的导航、标记和检查概念模型
  • 批准号:
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CGV: Medium: Collaborative Research: Developing conceptual models for navigation, marking, and inspection in the context of 3D image segmentation
CGV:媒介:协作研究:开发 3D 图像分割背景下的导航、标记和检查概念模型
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