Russakoff Daniel B, Hasegawa Akira
Fujifilm Software (California), Inc., San Jose, California, USA.
Med Image Comput Comput Assist Interv. 2006;9(Pt 2):454-61. doi: 10.1007/11866763_56.
Computer-aided detection (CAD) has become increasingly common in recent years as a tool in catching breast cancer in its early, more treatable stages. More and more breast centers are using CAD as studies continue to demonstrate its effectiveness. As the technology behind CAD improves, so do its results and its impact on society. In trying to improve the sensitivity and specificity of CAD algorithms, a good deal of work has been done on feature extraction, the generation of mathematical representations of mammographic features which can help distinguish true cancerous lesions from false ones. One feature that is not currently seen in the literature that physicians rely on in making their decisions is location within the breast. This is a difficult feature to calculate as it requires a good deal of prior knowledge as well as some way of accounting for the tremendous variability present in breast shapes. In this paper, we present a method for the generation and implementation of a probabilistic breast cancer atlas. We then validate this method on data from the Digital Database for Screening Mammography (DDSM).
近年来，计算机辅助检测（CAD）作为一种在乳腺癌早期更易治疗阶段进行检测的工具已变得越来越普遍。随着越来越多的研究不断证明其有效性，越来越多的乳腺中心正在使用CAD。随着CAD背后技术的改进，其结果及其对社会的影响也在提升。在试图提高CAD算法的敏感性和特异性方面，已经在特征提取方面做了大量工作，即生成乳腺X线摄影特征的数学表示，这有助于区分真正的癌性病变和假病变。目前在医生做决策时所依赖的文献中未出现的一个特征是乳房内的位置。这是一个难以计算的特征，因为它需要大量的先验知识以及某种方法来考虑乳房形状中存在的巨大变异性。在本文中，我们提出了一种生成和实施概率性乳腺癌图谱的方法。然后，我们在来自数字乳腺X线摄影筛查数据库（DDSM）的数据上验证了该方法。
