Multimodality CAD system with image references for breast mass characterization

描述（由申请人提供）：该项目的长期目标是开发一种有效的计算机辅助诊断（CAD）系统，以帮助放射科医生在乳腺成像中做出诊断决策。在这个拟议的项目中，我们将专注于使用乳房X线照片和超声图像的肿块的表征。我们提出了一种基于分类器的新CAD方法，该分类器可以同时估计肿块恶性的可能性，并从已知诊断的大型病例库中检索类似病例，供放射科医生参考。因此，新的CAD系统结合了基于评级和基于图像检索的CAD系统的优点。它将帮助放射科医生不仅恶性估计，但也通过提高他们的相似性为基础的决策过程。我们还将设计一个相关反馈图像检索系统，使放射科医生互动和有效地检索类似的情况下，从一个大的数据集作为一种工具，以帮助开发自动化CAD系统。我们假设，参考图像将增加定性的准确性，经验不足的读者群众，并在这项研究中开发的计算机化的分类和图像检索系统将显着提高放射科医生的准确性。为了验证这些假设，我们将执行以下具体任务：（1）收集包含肿块的超声图像和乳房X线照片的数据库;（2）提取肿块表征的特征;（3）开发决策树和k-最近邻分类器，比较有和没有提升的决策树训练，并研究基于开发的分类器检索相似病例的方法;（4）开发一种相关反馈图像检索方法;（5）比较经验不足的放射科医生在没有和有经验的放射科医生检索的参考图像的帮助下的表现;（6）通过受试者工作特征（ROC）研究比较放射科医生在没有和有全自动分类和图像检索CAD系统的情况下的表现。如果开发成功，CAD系统不仅可以减少良性活检，而且还可以减少经验丰富和经验不足的放射科医生之间的解释差异。该项目与公共卫生的相关性在于，70-85%的乳腺活检是针对良性病变进行的。在不降低乳腺癌检测灵敏度的情况下，这一数字的任何减少都将降低医疗保健成本，并通过减少焦虑和发病率来促进患者的健康。

DESCRIPTION (provided by applicant): The long term goal of the project is to develop an effective computer-aided diagnosis (CAD) system to assist radiologists in making diagnostic decisions in breast imaging. In this proposed project, we will concentrate on the characterization of masses using mammograms and ultrasound images. We propose a new approach to CAD based on a classifier that can simultaneously estimate the likelihood of malignancy for the mass and retrieve similar cases from a large library of cases with known diagnosis for the radiologist's references. The new CAD system thus combines the advantages of a rating-based and an image-retrieval- based CAD system. It will aid radiologists not only by the malignancy estimate but also by enhancing their similarity-based decision making process. We will also design a relevance feedback image retrieval system that allows the radiologist to interactively and efficiently retrieve similar cases from a large data set as a tool to help develop the automated CAD system. We hypothesize that the reference images will increase the characterization accuracy of less experienced readers for masses, and that the computerized classification and image retrieval system to be developed in this study will significantly improve radiologists' accuracy. To test these hypotheses, we will perform the following specific tasks: (1) collect a database of sonograms and mammograms containing masses; (2) extract features for mass characterization; (3) develop decision tree and k-nearest neighbor classifiers, compare decision tree training with and without boosting, and investigate methods for the retrieval of similar cases based on the developed classifiers; (4) develop a relevance feedback image retrieval method; (5) compare the performances of less experienced radiologists without and with aid by reference images retrieved by experienced radiologists; and (6) compare radiologists' performances without and with the fully-automated classification and image-retrieval CAD system by a receiver operating characteristic (ROC) study. If successfully developed, the CAD system may not only reduce benign biopsies, but also reduce the variation in interpretation between experienced and less experienced radiologists. The relevance of this project to public health is that 70-85% of breast biopsies are performed for benign lesions. Any reduction in this number without a decrease in breast cancer detection sensitivity will decrease health care costs, as well as contribute to the well-being of the patient by reducing anxiety and morbidity.