Electrical and Computer Systems Engineering Discipline, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, 47500, Subang Jaya, Selangor, Malaysia.
School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA.
Med Biol Eng Comput. 2021 Feb;59(2):355-367. doi: 10.1007/s11517-021-02313-1. Epub 2021 Jan 14.
This study objectively evaluates the similarity between standard full-field digital mammograms and two-dimensional synthesized digital mammograms (2DSM) in a cohort of women undergoing mammography. Under an institutional review board-approved data collection protocol, we retrospectively analyzed 407 women with digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) examinations performed from September 1, 2014, through February 29, 2016. Both FFDM and 2DSM images were used for the analysis, and 3216 available craniocaudal (CC) and mediolateral oblique (MLO) view mammograms altogether were included in the dataset. We analyzed the mammograms using a fully automated algorithm that computes 152 structural similarity, texture, and mammographic density-based features. We trained and developed two different global mammographic image feature analysis-based breast cancer detection schemes for 2DSM and FFDM images, respectively. The highest structural similarity features were obtained on the coarse Weber Local Descriptor differential excitation texture feature component computed on the CC view images (0.8770) and MLO view images (0.8889). Although the coarse structures are similar, the global mammographic image feature-based cancer detection scheme trained on 2DSM images outperformed the corresponding scheme trained on FFDM images, with area under a receiver operating characteristic curve (AUC) = 0.878 ± 0.034 and 0.756 ± 0.052, respectively. Consequently, further investigation is required to examine whether DBT can replace FFDM as a standalone technique, especially for the development of automated objective-based methods.
本研究客观评估了标准全数字化乳腺摄影与二维合成数字乳腺摄影（2DSM）在接受乳腺摄影的女性队列中的相似性。根据机构审查委员会批准的数据收集方案，我们回顾性分析了 2014 年 9 月 1 日至 2016 年 2 月 29 日期间接受数字乳腺断层合成摄影（DBT）和全数字化乳腺摄影（FFDM）检查的 407 名女性。FFDM 和 2DSM 图像均用于分析，共有 3216 份可用的头尾位（CC）和内外斜位（MLO）乳腺摄影图纳入数据集。我们使用完全自动化的算法分析乳腺摄影图，该算法计算 152 个结构相似性、纹理和基于乳腺摄影密度的特征。我们分别为 2DSM 和 FFDM 图像训练和开发了两种不同的基于全局乳腺图像特征分析的乳腺癌检测方案。在 CC 视图图像（0.8770）和 MLO 视图图像（0.8889）上计算的粗 Weber 局部描述符差分激励纹理特征分量上获得了最高的结构相似性特征。虽然粗结构相似，但基于全局乳腺图像特征的癌症检测方案在 2DSM 图像上的训练效果优于在 FFDM 图像上的训练效果，其接收者操作特征曲线下面积（AUC）分别为 0.878±0.034 和 0.756±0.052。因此，需要进一步研究以检查 DBT 是否可以替代 FFDM 作为独立技术，特别是用于开发自动化基于客观的方法。
