`III-CXT-Small: Collaborative Research: Structuring, Reasoning, and Querying in a Very Large Medical Image Database

`III-CXT-Small：协作研究：在非常大的医学图像数据库中构建、推理和查询

• 批准号: 0812120
• 负责人: Sharon Huang
• 金额: $ 22.95万
• 依托单位: Lehigh University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0812120&HistoricalAwards=false
• 关键词: III; CXT; Small; Collaborative; Research

Image data is of immense practical importance in medical informatics, and a subject of strong interest to researchers in industry and academia. While digital image databases are now prevalent in clinical and educational settings, and traditional means for interacting with and querying such collections can provide some level of useful functionality, there are few examples of systems that attempt to bridge the ?semantic gap.? The work proposed in this grant is a multi-institutional collaboration combining research in medical image processing, machine learning and pattern recognition, knowledge representation and querying, and evaluation by domain experts in the field, is intended to advance the state-of-the-art in this direction. The archive of 60,000 cervigram images assembled by the National Library of Medicine and National Cancer Institute is an ideal collection for this purpose. The NLM cervigram archive forms a narrow image domain that has a limited and predictable variability. In such cases, explicit representation of domain knowledge alleviates the semantic gap between the low-level sensory recordings of a scene (raw image data), and objects and processes implied from images (semantic interpretation). This research will follow an information hierarchy that proceeds from raw image data to low-level image features, recognition of objects and tissue types, knowledge-based reasoning about disease processes, and, finally, tools and visualizations to support diagnosis decisions by clinical and NLM/NCI collaborators. The research team will employ an underlying paradigm known as Computer-Assisted Visual Interactive Recognition, or CAVIAR, which considers the domain expert an integral part of the equation and attempts to optimize the performance of the complete human-machine system.Intellectual MeritImage content understanding is still considered a vexing open problem at the same time databases are growing rapidly in size and complexity. It is anticipated that this work will have a positive impact in areas relating to medical image analysis, including information extraction, organization, representation, and querying, as well as in training.Broader ImpactThrough the focus on the NLM/NCI cervigram archive, this research may help advance the role of cervicography as a more cost-effective procedure than pap smears and colposcopy in screening for cervical cancer. Results from this targeted-domain project could also illuminate gaps and help establish new priorities for research in broader domains such as multimedia content structuring, understanding, indexing, and retrieval.

图像数据在医学信息学中至关重要，并且是行业和学术界研究人员强烈感兴趣的主题。尽管数字图像数据库现在在临床和教育环境中很普遍，并且与此类集合进行互动和查询的传统手段可以提供一定程度的有用功能，但很少有系统的示例试图弥合语义差距？该赠款中提出的工作是一项多机构的合作，结合了医学图像处理，机器学习和模式识别，知识代表和查询的研究以及该领域专家的评估，旨在朝着这个方向发展最新的。由国家医学和国家癌症研究所组装的60,000个宫颈图像的档案是为此目的的理想收藏。 NLM宫颈存档形成一个狭窄的图像结构域，具有有限且可预测的可变性。在这种情况下，域知识的明确表示可以减轻场景的低级感觉记录（原始图像数据）与图像中隐含的对象和过程之间的语义差距（语义解释）。这项研究将遵循信息层次结构，该信息从原始图像数据到低级图像特征，对象和组织类型的识别，基于知识的疾病过程推理，最后是工具和可视化，以支持临床和NLM/NCI合作者的诊断决策。研究团队将采用一种潜在的范式，称为计算机辅助的视觉互动识别或鱼子酱，该范围认为域专家是方程式不可或缺的一部分，并试图优化完整的人机模型系统的性能。在同一时间数据库中，在同一时间和复杂性中，仍然认为，智能绩效图像的内容仍然被认为是一个令人愉悦的开放问题，并且在尺寸和复杂性方面都在增长。 It is anticipated that this work will have a positive impact in areas relating to medical image analysis, including information extraction, organization, representation, and querying, as well as in training.Broader ImpactThrough the focus on the NLM/NCI cervigram archive, this research may help advance the role of cervicography as a more cost-effective procedure than pap smears and colposcopy in screening for cervical cancer.这个目标域项目的结果还可以照亮差距，并有助于在诸如多媒体内容结构，理解，索引和检索等更广泛领域的研究中建立新的优先级。