Interactive CAD for Mammography

用于乳腺 X 线摄影的交互式 CAD

• 批准号: 7240502
• 负责人: Bin Zheng
• 金额: $ 16.36万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2010-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7240502
• 关键词: Address; Agreement; Algorithms; Benign; Biopsy; Boxing; Breast Microcalcification; Case Study; Characteristics; Classification; Computer Assisted; Cues; Databases; Detection; Diagnostic; Genetic Programming; Goals; Image; Institution; Libraries; Likelihood Functions; Malignant - descriptor; Malignant Neoplasms; Mammography; Medical; Numbers; Operating System; Other Finding; Patients; Performance; Rate; Reader; Reading; Reporting; Reproducibility; Risk Factors; Scheme; Score; Screening procedure; System; Testing; Training; Update; Weight; base; concept; design; digital; image processing; improved; radiologist; vector

DESCRIPTION (provided by applicant): Although commercial computer-aided detection (CAD) products have been used in a large number of medical institutions to assist radiologists in screening mammography, radiologists often have limited confidence in CAD cueing results for masses due to the relatively low sensitivity and reproducibility, as well as the higher false-positive rate, than that compared with the performance for microcalcification detection and characterization. There is no general agreement on whether and how a CAD system can best help improve radiologists' diagnostic performance. To address these issues, we propose to develop and evaluate a unique Interactive Computer-Aided Detection (ICAD) system for mammography. In addition to providing initial cues on the processed images as would current systems, radiologists can interact with the ICAD system in several ways. Observers can select any regions (cued or not cued) on the mammogram and query ICAD. Upon receiving a request from the observer, ICAD will extract the region, compute a feature vector, compare the vector with a large number of regions in a reference library, and generate a classification score using a weighted k-nearest neighbor algorithm. In addition, upon request, the system will provide the results of a scheme specifically optimized on "the latest prior images" to enable observers to assess "early signs" for abnormalities that may develop and later (on subsequent examinations) prove to be malignant. To test such a hypothesis, an observer performance study will also be carried out in this project to compare the performance difference when six radiologists use a CAD system operating at the performance level of leading commercial products and our newly proposed ICAD system.

描述（由申请人提供）： 尽管商业计算机辅助检测（CAD）产品已在大量医疗机构中用于协助放射科医生筛查乳腺X线摄影，但由于与微钙化检测和表征性能相比，其灵敏度和再现性相对较低，且假阳性率较高，因此放射科医生对肿块的CAD提示结果往往信心有限。对于 CAD 系统是否以及如何能够最好地帮助提高放射科医生的诊断性能，目前尚未达成普遍共识。为了解决这些问题，我们建议开发和评估独特的交互式计算机辅助检测（ICAD）系统用于乳腺X线摄影。除了像当前系统一样在处理后的图像上提供初始提示之外，放射科医生还可以通过多种方式与 ICAD 系统进行交互。观察者可以选择乳房 X 光照片上的任何区域（有提示或无提示）并查询 ICAD。收到观察者的请求后，ICAD 将提取区域，计算特征向量，将该向量与参考库中的大量区域进行比较，并使用加权 k-近邻算法生成分类分数。此外，根据要求，该系统将提供针对“最新先前图像”专门优化的方案结果，使观察者能够评估可能出现并随后（在随后的检查中）被证明是恶性的异常的“早期迹象”。为了检验这一假设，该项目还将进行观察者性能研究，以比较六名放射科医生使用以领先商业产品性能水平运行的 CAD 系统和我们新提出的 ICAD 系统时的性能差异。