Radiology Department of Radboud University Nijmegen Medical Centre, 6525 GA, Nijmegen, The
IEEE Trans Med Imaging. 2009 Dec;28(12):2033-41. doi: 10.1109/TMI.2009.2028611. Epub 2009 Aug 7.
When reading mammograms, radiologists do not only look at local properties of suspicious regions but also take into account more general contextual information. This suggests that context may be used to improve the performance of computer-aided detection (CAD) of malignant masses in mammograms. In this study, we developed a set of context features that represent suspiciousness of normal tissue in the same case. For each candidate mass region, three normal reference areas were defined in the image at hand. Corresponding areas were also defined in the contralateral image and in different projections. Evaluation of the context features was done using 10-fold cross validation and case based bootstrapping. Free response receiver operating characteristic (FROC) curves were computed for feature sets including context features and a feature set without context. Results show that the mean sensitivity in the interval of 0.05-0.5 false positives/image increased more than 6% when context features were added. This increase was significant ( p < 0.0001). Context computed using multiple views yielded a better performance than using a single view (mean sensitivity increase of 2.9%, p < 0.0001). Besides the importance of using multiple views, results show that best CAD performance was obtained when multiple context features were combined that are based on different reference areas in the mammogram.
在阅读乳房 X 光片时，放射科医生不仅要观察可疑区域的局部特征，还要考虑更一般的上下文信息。这表明上下文信息可能被用于提高计算机辅助检测（CAD）在乳房 X 光片中检测恶性肿块的性能。在这项研究中，我们开发了一组表示同一样本中正常组织可疑性的上下文特征。对于每个候选肿块区域，在当前图像中定义了三个正常参考区域。在对侧图像和不同的投影中也定义了相应的区域。使用 10 倍交叉验证和基于案例的引导来评估上下文特征。为包含上下文特征和不包含上下文特征的特征集计算了自由响应接收者操作特征（FROC）曲线。结果表明，当添加上下文特征时，在 0.05-0.5 个假阳性/图像的间隔内，平均敏感性增加了 6%以上。这种增加是显著的（p<0.0001）。使用多个视图计算的上下文比使用单个视图的性能更好（敏感性平均增加 2.9%，p<0.0001）。除了使用多个视图的重要性之外，结果还表明，当结合使用基于乳房 X 光片中不同参考区域的多个上下文特征时，可以获得最佳的 CAD 性能。
