Multi-view CAD System for Breast Cancer Early Detection

描述(由申请人提供)： 首次提出了一种新的完全优化方法，并在此阶段R21方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光检查中的肿块检测。最终优化的CAD系统将通过计算FROC分析作为回溯性案例研究进行评估，使用包含所有肿块类型的1200个四视图病例的单独图像数据库。R33阶段的研究项目是对R21阶段的优化单视图CAD算法进行改进，构建一个自适应的同侧多视图并行CAD系统，以提高乳腺癌的早期检测能力，重点是数字乳房X光摄影中微小肿块的计算机检测。全面优化新技术的可行性研究是通过R21阶段研究完成的，该阶段探索了单视图全模块计算机辅助诊断(CAD)算法的所有潜力，用于数字化屏幕/胶片乳房X光照相(SFM)和全场数字乳房X光照相(FFDM)中的肿块自动检测和诊断。根据最近的文献报道，为了获得较高的敏感度，CAD漏掉了早期乳腺癌并导致较高的假阳性(FP)检测率。这个项目的灵感来自于乳房X光师的解释程序。我们发现，异常诊断可以从多个视角得出，但不能通过单视角图像分析来实现。探索这一重要信息将导致CAD性能的显著提高。因此，本方案旨在对R21期项目中优化的同侧多视角数字化乳腺X线片的单视图CAD方法进行修改和优化，创建一个全新的多视图CAD方案。所提出的CAD系统的总体设计过程如下：对同侧乳房的每一幅图像，包括内侧斜视(MLO)和头尾(CC)视图，采用先进的预处理和分割方法进行处理，对多视图采用并发分析方法，从分割的可疑区域中提取特征，并分析不同视图之间的特征匹配。分析结果将反馈给单视图图像处理，供其进一步分析。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，以获得高敏感率。

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.