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

• 批准号: 1302248
• 负责人: Ruth West
• 金额: $ 29.63万
• 依托单位: University of North Texas
• 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
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1302248&HistoricalAwards=false
• 关键词: CGV; Medium; Collaborative; Research; Developing

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.

3D图像分割是面向图像的科学学科，尤其是生物医学的重要任务，其中图像为生物学发现提供了基础。 虽然成像技术揭示了整个主题中的空间内容和活动，但最终生物学家对特定的解剖结构（例如器官，组织，细胞等）感兴趣。 因此，在数据到知识管道中，在给定的图像集中划定感兴趣的结构是典型的第一步，既有分割的效率和准确性，都严重影响了在研究和临床实践中使用数据的方式。 创建准确的分割，尤其是对于3D生物医学图像，是一项非平凡的任务，要求人与计算机之间进行合作。 尽管人类专家以其出色的视觉感知能力以及从多年培训中获得的丰富知识和经验，但最终决定了什么构成准确的细分，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，分割算法在存在生物医学图像中不可避免的噪声和歧义的情况下很容易失败。 在这项研究中，PI将研究3D图像分割作为人类计算机相互作用范式，以更好地了解当前分割过程中涉及的人为因素，以使过程更有效，准确和可重复。 该团队的假设是，通过更深入地了解人们在3D细分的上下文中如何执行低级感知和认知任务（例如，视觉提示，通过商标对结构的分级，局部准确性或质量标准）以及域名专家的建筑物的建立（E. e.g shopeptions andement andement andement andement contement and toplestion），可以大大改善细分过程。 为了检验这一假设，PI将通过域专家分析分段过程，这些专家涵盖了实际分段的合理子空间以及生物学和临床实践中的分段任务，以定义一个概念框架，该概念框架捕获了低级感知和分割的认知元素，以及与高级信息有关的信息，以及与高级信息相关的信息以及与导航有关的信息以及标记，标记，标记，和检验。 该团队将在基础上进行实例化并实例化框架，将与专家合作开发一种原型细分工具，该工具探讨了新颖的互动和可视化范式及其支持算法。 原型工具将用于验证概念框架并创建更有效的切割解决方案。Boader的影响：通过将分割作为人类的看法和认知任务，这项工作代表了对任何分段算法或工具的现有研究的重大偏离。 由此产生的概念框架将成为两个社区之间的桥梁，从而为当前和未来的细分工具提供了更好的设计以及针对细分算法的新问题的框架。 对于最终用户，工作原型将支持由基本概念框架提供支持的更有效的细分体验。 此外，用户在处理细分问题时所具有的多种感知提示和概念模型的形式化将成为一个有用的测试案例，以理解当他们重新映射以解决从未为他们设计的问题时，人们对感知和认知如何相互作用的更一般性问题。 为了传播这项研究的发现，PI将将其工作原型发布为开源项目，然后可以用作算法开发人员，工具开发人员和最终用户之间的共享通信平台。