Multi-view CAD System for Breast Cancer Early Detection

用于乳腺癌早期检测的多视图 CAD 系统

• 批准号: 6894938
• 负责人: Wei Qian
• 金额: $ 49.92万
• 依托单位: H. LEE MOFFITT CANCER CTR & RES INST
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-05-16 至 2006-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6894938
• 关键词: bioengineering /biomedical engineering; bioimaging /biomedical imaging; breast neoplasm /cancer diagnosis; clinical research; computer assisted diagnosis; computer system design /evaluation; digital imaging; early diagnosis; female; human data; image processing; mammography; statistics /biometry

DESCRIPTION (provided by applicant): A novel full optimization methodology is originally proposed and the further development and optimization of a new class of computer-aided diagnosis (CAD) methods is designed for mass detection in digital mammography in this Phase R21 proposal. The final optimized CAD system will be evaluated by computed FROC analysis as a retrospective case study, using a separate image data base including 1200 four view cases containing all mass types. This Phase R33 research project is to modify the optimized single-view CAD algorithms from Phase R21 for constructing an adaptive ipsilateral multi-view concurrent CAD system for improving the early detection of breast cancer by focusing on the computerized detection of tiny mass in digital mammography. The feasibility study of the full optimization new technology is achieved through the Phase R21 research, which explores all the potentials for the single-view full modular computer-aided diagnosis (CAD) algorithms for the automatic detection and diagnosis of masses from digitized screen/film mammography (SFM) and full field digital mammography (FFDM). As worldwide reported in recent literature, the CAD misses early stage breast cancer and results in a relatively large false-positive (FP) detection rate in order to achieve a high sensitivity rate. This project is inspired by the interpretation procedure from mammographers. We found that the abnormal diagnosis can be derived from multiple views but not available through single-view image analysis. To explore this important information will result in significant improvements on CAD performance. In consequence, this proposal aims at modification and optimization of the single-view CAD methodologies optimized in Phase R21 project for ipsilateral multi-view digital mammograms, which creates an entire new multi-view CAD scheme. The overall design procedure of proposed CAD system is as follows: each view image of ipsilateral breast including mediolateral oblique (MLO) view and craniocaudal (CC) view will be processed using advanced preprocessing and segmentation methods, concurrent analysis method will be employed in multi-view images to extract features from segmented suspicious regions and to analyze the feature matching between different views. The analysis result will be fed back to single-view image processing for their further analysis. Such iterative processing and analysis will be conducted between single-view and multi-view images to differentiate tiny suspicious regions for early stage breast cancer detection and reducing false-positive (FP) detection rate in order to achieve a high sensitivity rate.

描述（由申请人提供）： 最初提出了一种新的全面优化方法，并在该R21阶段提案中设计了一类新的计算机辅助诊断（CAD）方法的进一步开发和优化，用于数字乳腺X射线摄影中的肿块检测。最终优化的CAD系统将通过计算FROC分析进行评估，作为回顾性病例研究，使用单独的图像数据库，包括1200个包含所有肿块类型的四视图病例。本R33期研究项目旨在修改R21期的优化单视图CAD算法，以构建自适应同侧多视图并发CAD系统，通过专注于数字乳腺X射线摄影中微小肿块的计算机化检测来改善乳腺癌的早期检测。全面优化新技术的可行性研究是通过R21阶段研究实现的，该研究探索了单视图全模块化计算机辅助诊断（CAD）算法的所有潜力，用于从数字化屏幕/胶片乳腺X射线摄影（SFM）和全视野数字乳腺X射线摄影（FFDM）中自动检测和诊断肿块。如最近文献中的全球报道，CAD错过了早期乳腺癌，并导致相对较大的假阳性（FP）检出率，以实现高灵敏度。这个项目的灵感来自于乳房摄影师的解释程序。我们发现，异常诊断可以从多个视图，但不能通过单视图图像分析。探索这一重要信息将导致CAD性能的显着改善。因此，该提案旨在修改和优化在R21阶段项目中针对同侧多视图数字乳腺X线摄影优化的单视图CAD方法，这创建了全新的多视图CAD方案。提出的CAD系统的总体设计过程如下：同侧乳房的每个视图图像，包括内外斜（MLO）视图和头尾（CC）视图将使用先进的预处理和分割方法进行处理，将采用并发分析方法在多视图图像中提取特征从分割的可疑区域，并分析不同视图之间的特征匹配。分析结果将反馈到单视图图像处理中进行进一步分析。这种迭代处理和分析将在单视图和多视图图像之间进行，以区分用于早期乳腺癌检测的微小可疑区域，并降低假阳性（FP）检测率，以实现高灵敏度。