A suite of diagnostic aids based on image retrieval

一套基于图像检索的诊断辅助工具

• 批准号: 8119450
• 负责人: Yongyi Yang
• 金额: $ 32万
• 依托单位: ILLINOIS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8119450
• 关键词: Algorithms; Breast Microcalcification; Caring; Chicago; Classification; Clinical; Computer software; Computer-Assisted Diagnosis; Computers; Data; Data Set; Databases; Decision Making; Detection; Development; Diagnosis; Diagnostic; Ensure; Environment; Evaluation; Evaluation Studies; Feedback; Florida; Funding; Health; Hospital Information Systems; Hospitals; Human; Image; Image Analysis; Imagery; Label; Lead; Learning; Lesion; Libraries; Machine Learning; Malignant Neoplasms; Mammography; Measures; Medical Imaging; Methods; Metric; Mining; Modeling; Pathology; Patients; Performance; Philosophy; Probability; Process; Radiology Specialty; Reader; Research; Retrieval; Software Tools; System; Systems Development; Techniques; Time; Training; Universities; base; case-based; computerized; design; follow-up; imaging modality; improved; indexing; novel; preference; radiologist; success; tool; vector

DESCRIPTION (provided by applicant): The predominant approach to computer-aided diagnosis (CAD) in medical imaging has been to use automated image analysis to serve as a "second reader," with the aim of improving radiologists' diagnostic performance. CAD techniques traditionally aim to highlight suspicious lesions (called CADe) and/or estimate diagnostic variables, such as probability of malignancy (called CADx). We have been developing and evaluating a different approach to CAD, in which the radiologist will be assisted by a content-based search engine that will automatically identify and display examples of lesions, with known pathology, that are similar to the lesion being evaluated (referred to as the query). This will involve searching a large database for the images that are most similar to the query, based on image features that are automatically extracted by the software. The philosophy of this approach is to help inform the radiologist's diagnosis in difficult cases by presenting relevant information from past cases. The retrieved example lesions will allow the radiologist to explicitly compare known cases to the unknown case. A key advantage of the proposed retrieval approach to CAD is that it leaves decision-making entirely in the hands of the radiologist, unlike CADx, which acts as a supplemental decision maker. In our approach, we aim to tackle the key challenge of image retrieval, which is to develop a meaningful computerized measure of the similarity (relevance) of a patient's images to other images in the database. Departing from typical approaches based on numerical distance measures, we have proposed that the most useful measure of similarity is one that is designed specifically to match that perceived by the radiologist. We postulate that the radiologist's notion of similarity is some complicated unknown function of the images, and use advanced machine-learning algorithms to learn this function from similarity scores collected from radiologists in reader studies. Under R21 funding, we successfully demonstrated the feasibility and good performance of our approach in small data sets. The purpose of this proposed R01 project is to follow up the R21 project with a significantly larger scale effort in order to bring this approach to fruition, which will lead to a suite of retrieval-based CAD tools. We will develop the following unique components toward a clinical diagnostic aid: 1) instead of using indexing terms or simple distance measures to identify relevant images in the database, the system will use a similarity measure specifically trained to match radiologists' notion of relevance, as inferred from data obtained in an observer study; 2) in addition to presenting the retrieved cases to the radiologist, the system will use them to boost a CADx classifier to improve its classification accuracy on the query lesion; 3) the system will have the new capability of automatically building a large reference library by extracting known cases from a hospital PACS, thereby maximizing the benefit by retrieving more-similar cases; and 4) the system will be augmented with a highly interactive interface, which will include new tools for automatically adapting the similarity measure according to users' preferences, and for effectively presenting retrieved results. All of these components are novel and important to ultimate success of this kind of diagnostic aid. The project will include a preliminary demonstration using the Hospital Information System at the University of Chicago Hospitals, and will include preliminary evaluation studies to determine the effect of the system on radiologists' diagnostic performance. PUBLIC HEALTH RELEVANCE: This project will focus on development of a suite of supporting tools to facilitate the interpretation of images in radiology by mining similar cases from a database. The proposed system will make available to the radiologist through an intuitive interface a broad selection of relevant past cases to the one being diagnosed, along with an improved measure of its malignancy that is boosted by using retrieved cases, from which the radiologist can draw his or her own conclusions. We hypothesize that by providing such case-based evidence it will help radiologists in their decision-making process, particularly in diagnosis of difficult cases.

描述（由申请人提供）：医学成像中计算机辅助诊断（CAD）的主要方法是使用自动图像分析作为“第二阅读器”，目的是提高放射科医生的诊断性能。 CAD 技术传统上旨在突出可疑病变（称为 CADe）和/或估计诊断变量，例如恶性肿瘤的概率（称为 CADx）。我们一直在开发和评估一种不同的 CAD 方法，其中放射科医生将得到基于内容的搜索引擎的协助，该引擎将自动识别并显示具有已知病理学且与正在评估的病变相似的病变示例（参考作为查询）。这将涉及根据软件自动提取的图像特征在大型数据库中搜索与查询最相似的图像。这种方法的理念是通过提供过去病例的相关信息来帮助放射科医生对疑难病例进行诊断。检索到的示例病变将允许放射科医生明确地将已知病例与未知病例进行比较。所提出的 CAD 检索方法的一个关键优势是，它将决策完全交给放射科医生，这与充当补充决策者的 CADx 不同。在我们的方法中，我们的目标是解决图像检索的关键挑战，即开发一种有意义的计算机化方法来衡量患者图像与数据库中其他图像的相似性（相关性）。与基于数字距离测量的典型方法不同，我们提出最有用的相似性测量是专门为匹配放射科医生的感知而设计的测量。我们假设放射科医生的相似性概念是图像的某种复杂的未知函数，并使用先进的机器学习算法从放射科医生在读者研究中收集的相似性分数来学习该函数。在 R21 资助下，我们成功证明了我们的方法在小数据集上的可行性和良好性能。拟议的 R01 项目的目的是在 R21 项目的基础上进行更大规模的努力，以使这种方法取得成果，从而产生一套基于检索的 CAD 工具。我们将为临床诊断辅助开发以下独特的组件：1）系统将使用专门训练的相似性度量来匹配放射科医生的相关性概念，而不是使用索引术语或简单的距离度量来识别数据库中的相关图像，如下所示根据观察者研究中获得的数据推断； 2) 除了将检索到的病例呈现给放射科医生之外，系统还将使用它们来增强 CADx 分类器，以提高其对查询病变的分类准确性； 3）系统将具有通过从医院PACS中提取已知病例自动构建大型参考库的新功能，从而通过检索更多相似病例来最大化效益； 4) 该系统将增强一个高度交互的界面，其中将包括用于根据用户偏好自动调整相似性度量并有效呈现检索结果的新工具。所有这些组件都是新颖的，对于此类诊断辅助设备的最终成功非常重要。该项目将包括使用芝加哥大学医院的医院信息系统进行初步演示，并将包括初步评估研究，以确定该系统对放射科医生诊断表现的影响。公共卫生相关性：该项目将重点开发一套支持工具，通过从数据库中挖掘类似病例来促进放射学图像的解释。拟议的系统将通过直观的界面向放射科医生提供与正在诊断的患者相关的过去相关病例的广泛选择，以及通过使用检索到的病例增强的对其恶性程度的改进测量，放射科医生可以从中提取他的或她自己的结论。我们假设，通过提供此类基于病例的证据，它将有助于放射科医生的决策过程，特别是在诊断疑难病例时。