权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

CAREER: Low Latency, Parallel, and Context Aware Vision in Computed Tomography

职业：计算机断层扫描中的低延迟、并行和上下文感知视觉

基本信息

批准号：
1553436
负责人：
James Shackleford
金额：
$ 47.25万
依托单位：
Drexel University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-05-15 至 2022-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1553436&HistoricalAwards=false
关键词：
CAREER Low Latency Parallel Context

项目摘要

The increasing availability and maturity of non-invasive volumetric imaging techniques has provided the scientific, engineering, and medical communities with powerful methods of acquiring dense quantitative representations of both animate and inanimate objects---providing us with powerful methods of information capture. This research effort concentrates on approaches for identifying and labeling representations within the captured data that integrate a priori structural and spatial configuration knowledge. The automatic identification and labeling of volumetric pixels (voxels) produced by volumetric scanners presents challenging ill-posed problems that presently remain unsolved. Even for well trained clinicians, determining organ boundaries when applying anatomical labels to low contrast medical images can be highly subjective, despite possessing strong prior anatomical knowledge of structure and shape. Consequently, an anatomical delineation performed by different clinicians, even for the same patient image, fails to provide consistency. More difficult ill-posed problems, such as matching corresponding voxels between images of a patient taken at different times---or, more difficult yet, between two entirely different patients---are so subjective that humans rarely provide consistent answers. This research effort aims to develop algorithmic solutions to these problems in order to provide consistent quantitative analysis across volumes; thereby removing subjectivity attributable to inattentional blindness or unintentional bias. With further advancement, such algorithms will be adequately fast and robust to extract anatomic structural information and perform patient correspondence autonomously at massive scales; thereby enabling powerful data analytics paving the way for the future of data driven medicine. Through classroom integration and curriculum development, the PI will train science and engineering students to work with this next generation of image processing algorithms. The PI will recruit and mentor researchers at both the undergraduate and graduate levels with emphasis on the recruitment of underrepresented groups within STEM related fields. Therefore, this research aligns with the NSF mission to promote the progress of science and to advance the national health, prosperity and welfare.This research project consists of three primary efforts: first, the simultaneous segmentation of multiple structures using spatial relationship priors (i.e. situationally aware segmentation); second, the development of structurally aware registration algorithms (leveraging segmentation results); and third, the development of these algorithms specifically targeted to data parallel computer architectures. Situationally aware algorithms developed by the PI will aim to perform simultaneous multi-target segmentations as well as anatomically specialized inference of organ deformation and motion that are robust to imaging inconsistencies, setup variations, and low-dose imaging. The PI will investigate algorithmic methods possessing robustness to noisy, incomplete, or otherwise challenging image acquisitions by solving multiple inverse problems simultaneously and achieving higher accuracy through the coupling of solutions and exploitation of signal sparsity under certain basises. The algorithms produced by this research will have broad societal impacts on fields employing computed tomography including archeology, soil sciences, the timber industry, biological sciences, industrial X-ray based inspection, and the aviation security industry. In addition to the dissemination of the novel algorithms, the PI will develop high-performance parallel implementations as a library released under a permissive open-source license. Development will occur in the open using Git; thereby enabling agile decentralized development that encourages increased utilization by and contributions from scientists and developers extramural to the project. Algorithmic facilities initially selected for inclusion are areas of principal investigator's expertise and cover a wide spectrum of applications including motion estimation, image stitching, segmentation, 3D volume reconstruction (computed tomography), and registration/image fusion. Through consortia and workshops, domain experts will be encouraged to contribute their expertise in established and emerging fields (e.g. digital image forensics); enabling scientific cross-fertilization and collaboration across domain specific fields.

非侵入性容积成像技术的日益成熟为科学、工程和医学界提供了获取有生命和无生命物体的密集定量表示的强大方法-为我们提供了强大的信息捕获方法。这项研究工作集中在识别和标记的方法表示内捕获的数据，集成了先验的结构和空间配置知识。自动识别和标记的体积扫描仪产生的体积像素（体素）提出了具有挑战性的不适定的问题，目前仍然没有解决。即使对于受过良好训练的临床医生，在将解剖标记应用于低对比度医学图像时确定器官边界也可能是高度主观的，尽管具有关于结构和形状的强先验解剖知识。因此，由不同的临床医生执行的解剖描绘，即使对于相同的患者图像，也不能提供一致性。更困难的不适定问题，例如匹配不同时间拍摄的患者图像之间的相应体素-或者，更困难的是，在两个完全不同的患者之间-是如此主观，人类很少提供一致的答案。这项研究工作的目的是开发这些问题的算法解决方案，以提供跨卷一致的定量分析;从而消除归因于无意失明或无意偏见的主观性。随着进一步的发展，这些算法将足够快速和强大，以提取解剖结构信息并在大规模上自主执行患者对应;从而实现强大的数据分析，为数据驱动医学的未来铺平道路。通过课堂整合和课程开发，PI将培训科学和工程专业的学生使用下一代图像处理算法。 PI将在本科和研究生阶段招募和指导研究人员，重点是在STEM相关领域招募代表性不足的群体。因此，本研究符合美国国家科学基金会（NSF）推动科学进步和促进国民健康、繁荣和福利的使命，主要包括三个方面的工作：第一，利用空间关系先验对多个结构进行同步分割（即情境感知分割）;第二，结构感知配准算法的开发（利用分割结果）;第三，这些算法的开发专门针对数据并行计算机架构。 PI开发的情境感知算法旨在执行同时多目标分割以及器官变形和运动的解剖学专业推断，这些算法对成像不一致性、设置变化和低剂量成像具有鲁棒性。 PI将研究算法方法，该算法方法通过同时解决多个逆问题并通过耦合解决方案和在某些基础上利用信号稀疏性来实现更高的精度，从而对噪声、不完整或其他具有挑战性的图像采集具有鲁棒性。这项研究产生的算法将对采用计算机断层扫描的领域产生广泛的社会影响，包括考古学，土壤科学，木材工业，生物科学，基于工业X射线的检查和航空安全行业。除了传播新的算法，PI还将开发高性能的并行实现，作为一个在开放源码许可下发布的库。开发将使用Git在开放的环境中进行;从而实现敏捷的去中心化开发，鼓励科学家和开发人员增加对项目的利用和贡献。最初选择纳入的医学设施是主要研究者的专业领域，涵盖了广泛的应用，包括运动估计，图像拼接，分割，3D体积重建（计算机断层扫描），配准/图像融合。通过联盟和研讨会，将鼓励领域专家在现有和新兴领域（如数字图像取证）贡献他们的专业知识;使科学交叉和跨领域的具体领域的合作成为可能。