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“第二读者”，以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，在该方法中，放射科医生将由基于内容的搜索引擎提供帮助，该引擎将自动识别和显示具有已知病理学的病变的例子，与所评估的病变相似（称为查询）。这将涉及基于软件自动提取的图像功能，搜索与查询最相似的图像的大数据库。这种方法的理念是通过介绍过去病例中的相关信息来帮助放射科医生的诊断。检索到的例子病变将使放射科医生明确将已知病例与未知病例进行比较。拟议的CAD检索方法的一个主要优点是，它完全将决策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，将患者图像的相似性（相关性）与数据库中的其他图像开发出有意义的计算机化度量。我们脱离了基于数值距离度量的典型方法，我们提出，最有用的相似性措施是专门设计用于匹配放射线医生的方法。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，并使用先进的机器学习算法从读者研究中从放射线学家那里收集的相似性得分来学习此功能。在R21资金下，我们成功证明了在小型数据集中我们的方法的可行性和良好性能。该建议的R01项目的目的是以更大的规模努力跟进R21项目，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措施来识别数据库中的相关图像，而是使用专门培训的相似性措施来匹配放射线医生的相关性，从观察者研究中获得的数据推断出来； 2）除了向放射科医生提出的病例外，该系统还将使用它们来提高CADX分类器以提高其在查询病变上的分类准确性； 3）该系统将具有新的能力，可以通过从医院PAC中提取已知病例自动构建大型参考文献，从而通过检索更相似的情况来最大程度地提高益处； 4）系统将通过高度交互式界面进行增强，该界面将包括新工具，以根据用户的喜好自动调整相似度度量，并有效地显示结果。所有这些组件都是新颖的，对于这种诊断援助的最终成功而言至关重要。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性能的影响。公共卫生相关性：该项目将着重于开发一套支持工具，以通过从数据库中挖掘类似情况来促进放射学中图像的解释。所提出的系统将通过直观的界面向放射科医生提供广泛的相关案例，并通过使用检索的病例来提高其恶性肿瘤的改进措施，而放射线医生可以从中得出他或她自己的结论。我们假设，通过提供此类基于案例的证据，它将帮助放射科医生进行决策过程，尤其是在诊断困难案件的诊断过程中。