Timp Sheila, Varela Celia, Karssemeijer Nico
Dacolian, 9410 AD Beilen, The Netherlands.
IEEE Trans Med Imaging. 2007 Jul;26(7):945-53. doi: 10.1109/TMI.2007.897392.
In this paper, we present a fully automated computer-aided diagnosis (CAD) program to detect temporal changes in mammographic masses between two consecutive screening rounds. The goal of this work was to improve the characterization of mass lesions by adding information about the tumor behavior over time. Towards this goal we previously developed a regional registration technique that finds for each mass lesion on the current view a location on the prior view where the mass was most likely to develop. For the task of interval change analysis, we designed two kinds of temporal features: difference features and similarity features. Difference features indicate the (relative) change in feature values determined on prior and current views. These features may be especially useful for lesions that are visible on both views. Similarity features measure whether two regions are comparable in appearance and may be useful for lesions that are visible on the prior view as well as for newly developing lesions. We evaluated the classification performance with and without the use of temporal features on a dataset consisting of 465 temporal mammogram pairs, 238 benign, and 227 malignant. We used cross validation to partition the dataset into a training set and a test set. The training set was used to train a support vector machine classifier and the test set to evaluate the classifier. The average A(z) value (area under the receiver operating characteristic curve) for classifying each lesion was 0.74 without temporal features and 0.77 with the use of temporal features. The improvement obtained by adding temporal features was statistically significant (P = 0.005). In particular, similarity features contributed to this improvement. Furthermore, we found that the improvement was comparable for masses that were visible and for masses that were not visible on the prior view. These results show that the use of temporal features is an effective approach to improve the characterization of masses.
在本文中，我们提出了一种全自动计算机辅助诊断（CAD）程序，用于检测连续两次筛查轮次之间乳腺钼靶肿块的时间变化。这项工作的目标是通过添加有关肿瘤随时间变化行为的信息来改善肿块病变的特征描述。为实现这一目标，我们先前开发了一种区域配准技术，该技术可为当前视图上的每个肿块病变找到其在先前视图上最可能出现的位置。对于间隔变化分析任务，我们设计了两种时间特征：差异特征和相似性特征。差异特征表示在先前视图和当前视图上确定的特征值的（相对）变化。这些特征对于在两个视图上均可见的病变可能特别有用。相似性特征衡量两个区域在外观上是否可比，对于在先前视图上可见的病变以及新出现的病变可能有用。我们在一个由465对乳腺钼靶时间序列图像组成的数据集上评估了使用和不使用时间特征时的分类性能，其中包括238例良性病变和227例恶性病变。我们使用交叉验证将数据集划分为训练集和测试集。训练集用于训练支持向量机分类器，测试集用于评估分类器。在不使用时间特征的情况下，对每个病变进行分类的平均A(z)值（接收器操作特征曲线下的面积）为0.74，使用时间特征时为0.77。添加时间特征所获得的改进具有统计学意义（P = 0.005）。特别是，相似性特征促成了这一改进。此外，我们发现对于在先前视图上可见的肿块和不可见的肿块，这种改进是相当的。这些结果表明，使用时间特征是改善肿块特征描述的有效方法。
