A suite of diagnostic aids based on image retrieval

一套基于图像检索的诊断辅助工具

基本信息

批准号：
8300707
负责人：
Yongyi Yang
金额：
$ 32.03万
依托单位：
ILLINOIS INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-08-01 至 2014-07-31
项目状态：
已结题

项目摘要

Project Summary 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.

项目摘要医学成像中计算机辅助诊断（CAD）的主要方法是使用自动化图像分析是“第二读者”，目的是改善放射科医生的诊断性能。传统上，CAD技术旨在突出可疑病变（称为CADE）和/或估算诊断变量，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，放射科医生将是在基于内容的搜索引擎的协助下，该引擎将自动识别和显示病变的示例已知的病理，类似于正在评估的病变（称为查询）。这将涉及基于图像功能由软件自动提取。这种方法的理念是帮助放射科医生的在困难案例中诊断，通过提供过去病例中的相关信息。检索的示例病变将允许放射科医生明确将已知病例与未知病例进行比较。一个关键优势拟议的检索方法是，它将决策完全留在了放射科医生的手中，与CADX不同，CADX充当补充决策者。在我们的方法中，我们旨在应对图像检索的关键挑战，即发展有意义患者图像与数据库中其他图像的相似性（相关性）的计算机测量。我们脱离了基于数值距离措施的典型方法，我们提出了最多的方法相似性的有用度量是专门针对放射科医生所感知的。我们假设放射科医生的相似性概念是图像的某些复杂的未知功能，并且使用先进的机器学习算法从收集的相似性分数中学习此功能读者研究的放射学家。根据R21的资金，我们成功地证明了我们方法的可行性和良好表现小数据集。该建议的R01项目的目的是跟进R21项目更大的努力以使这种方法实现，这将导致一套基于检索的CAD 工具。我们将开发以下独特的组件，用于临床诊断帮助：1）而不是使用索引术语或简单的距离度量以识别数据库中相关图像，系统将使用从获得的数据中推断出的相似性措施，专门训练以匹配放射科医生的相关性概念在观察者研究中； 2）除了将检索到的病例介绍给放射科医生外，系统还将使用它们可以提高CADX分类器，以提高其在查询病变上的分类精度； 3）系统将具有新的能力，可以通过从一个中提取已知案例自动构建大型参考库医院PACS，从而通过检索更相似的病例来最大程度地提高利益； 4）系统将是增强具有高度交互式界面，该界面将包括用于自动调整的新工具根据用户的喜好进行相似性度量，并有效地提出检索结果。所有这些组件是新颖的，对于这种诊断援助的最终成功而言很重要。该项目将包括使用大学医院信息系统的初步演示芝加哥医院，并将包括初步评估研究，以确定系统对放射科医生的诊断性能。