Novel Methods for Automated Key Image Selection

自动关键图像选择的新方法

• 批准号: 6583176
• 负责人: JOHN N DIONISIO
• 金额: $ 9.34万
• 依托单位: MEDAXIS CORPORATION
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-01-15 至 2004-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6583176
• 关键词: archives; biomedical automation; clinical research; computer data analysis; computer program /software; computer system design /evaluation; data management; human data; image processing; method development

DESCRIPTION (provided by the applicant): Significant new knowledge about human behavior and the brain has come to light in recent years, due in part to rapid technical developments in imaging. As the role of imaging becomes increasingly important in neurosciences, effective methods for managing and retrieving images will become even more critical; without such advances, further progress will be hindered. The goal of this proposal is the automated summarization of large imaging sets. Image summarization proffers a method to compress imaging studies by selecting only pertinent image slices that objectively document a patient's condition; as such, its applications include multimedia electronic medical records, telemedicine, and teaching files. In Phase I, development is focused on a customizable brain atlas used for registering patient imaging studies in order to select key images. This phase addresses selection of images from "normal" studies and studies with only subtle morphological changes, as typical of most patients with psychiatric disorders. Automatic techniques for customizing the atlas to imaging study acquisition parameters are developed, in addition to registration methods for mapping the atlas to the patient's original study. Building from this initial work, Phase II expands to encompass selection of images from "abnormal" studies that exhibit gross morphological changes through principle component analysis, further customization of the atlas for different age groups (e.g., pediatric), and incorporation of structured data entry (SDE) and natural language processing (NLP) of medical reports to help guide automatic selection of key images. The resultant product will be a fully automated software system that can select relevant images from any imaging study. Initial evaluation in Phase I will examine the performance of the contrast customizable atlas and summarization/relevant slice selection, as compared to human experts.

描述（由申请人提供）：近年来，有关人类行为和大脑的重要新知识已经曝光，部分原因是成像技术的快速发展。随着成像在神经科学中的作用变得越来越重要，管理和检索图像的有效方法将变得更加重要;如果没有这些进展，进一步的进展将受到阻碍。该提案的目标是自动总结大型成像集。图像摘要提供了一种方法来压缩成像研究，只选择相关的图像切片，客观地记录病人的病情，因此，它的应用包括多媒体电子病历，远程医疗和教学文件。在第一阶段，开发的重点是一个可定制的大脑图谱，用于注册患者成像研究，以选择关键图像。这一阶段解决了从“正常”研究和只有细微形态变化的研究中选择图像的问题，这是大多数精神疾病患者的典型特征。除了用于将图谱映射到患者的原始研究的配准方法之外，还开发了用于将图谱定制到成像研究采集参数的自动技术。在这一初步工作的基础上，第二阶段扩展到包括从“异常”研究中选择图像，这些研究通过主成分分析显示出总体形态学变化，进一步定制不同年龄组的图谱（例如，儿科），并结合结构化数据输入（RISK）和自然语言处理（NLP）的医疗报告，以帮助指导自动选择关键图像。由此产生的产品将是一个完全自动化的软件系统，可以从任何成像研究中选择相关图像。阶段I中的初始评估将检查对比度可定制图谱和总结/相关切片选择的性能，与人类专家相比。