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

III-CXT-Small：协作研究：在超大型医学图像数据库中构建、推理和查询

• 批准号: 0812073
• 负责人: Gang Tan
• 金额: $ 5.45万
• 依托单位: Boston College
• 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 2008-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0812073&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 Merit Image 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 Impact Through 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具有巨大的实际重要性，并且是工业界和学术界研究人员强烈感兴趣的主题。虽然数字图像数据库现在在临床和教育环境中盛行，并且与此类集合进行交互和查询的传统方法可以提供某种程度的有用功能，但尝试连接？语义鸿沟？这项资助中提出的工作是一项多机构合作，结合了医学图像处理，机器学习和模式识别，知识表示和查询以及该领域专家的评估研究，旨在推进这一方向的最新技术。国家医学图书馆和国家癌症研究所收集的60，000张宫颈照片的档案是实现这一目的的理想收藏。NLM宫颈造影档案形成了一个狭窄的图像域，具有有限的和可预测的可变性。在这种情况下，领域知识的显式表示消除了场景（原始图像数据）的低级感官记录与图像（语义解释）中隐含的对象和过程之间的语义差距。这项研究将遵循一个信息层次结构，从原始图像数据到低级别图像特征，对象和组织类型的识别，关于疾病过程的基于知识的推理，以及最后的工具和可视化，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称为计算机辅助视觉交互识别（CAVIAR）的基础范式，该范式将领域专家视为等式的一个组成部分，并试图优化整个人机系统的性能。在数据库的规模和复杂性迅速增长的同时，图像内容理解仍然被认为是一个令人烦恼的开放问题。预计这项工作将在与医学图像分析有关的领域产生积极影响，包括信息提取、组织、表示和查询以及培训。更广泛的影响通过对NLM/NCI宫颈造影档案的关注，这项研究可能有助于提高宫颈造影术在宫颈癌筛查中的作用，使其成为比巴氏涂片和阴道镜更具成本效益的程序。这个目标领域项目的结果也可以照亮差距，并帮助建立新的优先事项，在更广泛的领域，如多媒体内容结构，理解，索引和检索的研究。