`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具有巨大的实际重要性，也是工业界和学术界研究人员浓厚兴趣的主题。虽然数字图像数据库现在在临床和教育环境中很普遍，并且与此类集合交互和查询的传统方法可以提供一定程度的有用功能，但尝试弥合“语义差距”的系统示例很少。这项资助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领域专家的评估方面的研究，旨在推进最先进的技术艺术朝这个方向发展。国家医学图书馆和国家癌症研究所收集的 60,000 张宫​​颈照片档案是实现此目的的理想收藏。 NLM 宫颈图档案形成了一个狭窄的图像域，具有有限且可预测的可变性。在这种情况下，领域知识的显式表示减轻了场景的低级感知记录（原始图像数据）与图像隐含的对象和过程（语义解释）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图像特征、物体和组织类型的识别、关于疾病过程的基于知识的推理，最后是支持临床和 NLM 诊断决策的工具和可视化/NCI 合作者。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方程式的一个组成部分，并尝试优化整个人机系统的性能。仍然被认为是一个令人烦恼的开放问题，同时数据库的规模和复杂性正在迅速增长。预计这项工作将对医学图像分析相关领域产生积极影响，包括信息提取、组织、表示和查询以及培训。更广泛的影响通过关注 NLM/NCI 宫颈图档案，这项研究可能有助于提高宫颈造影作为比子宫颈抹片检查和阴道镜检查更具成本效益的程序在宫颈癌筛查中的作用。这个目标领域项目的结果还可以阐明差距，并帮助在更广泛的领域（例如多媒体内容结构、理解、索引和检索）确定新的研究重点。