Volumetric Breast Density Combined with Masking Risk: Enhanced Characterization of Breast Density from Mammography Images

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• 关键词：Mammography ; Breast density ; Masking ; BI ; RADS 5th edition
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9699
• 期：1
• 页码：486-492
• 全文大小：2,291 KB
• 参考文献：1.Boyd, N.F., Guo, H., Martin, L.J., Sun, L., Stone, J., Fishell, E., Jong, R.A., Hislop, G., Chiarelli, A., Minkin, S., Yaffe, M.J.: Mammographic density and the risk and detection of breast cancer. N. Engl. J. Med. 356(3), 227–236 (2007)CrossRef
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• 作者单位：Andreas Fieselmann (16)
  Anna K. Jerebko (16)
  Thomas Mertelmeier (16)
  
  16. Siemens Healthcare GmbH, Erlangen, Germany
  
• 丛书名：Breast Imaging
• ISBN：978-3-319-41546-8
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9699

文摘

Automatic characterization of breast density can enable more personalized breast cancer screening work flows. In this work, we present a novel method to automatically characterize breast density in mammography images. Our method computes a volumetric density map and measures the relative volume of glandular tissue (VBD%). For critical cases when masking of small masses may be possible it additionally quantifies the masking effect of glandular tissue. VBD% and the masking risk combined provide a 4-point density score that correlates with the BI-RADS 5th edition guidelines. We evaluated our approach using a study with 32 radiologists and 2400 breast images (600 4-view FFDM exams). In a subset of 415 images identified as critical cases the accuracy to detect dense breasts (density categories c or d) increased as shown by the area under the curves (0.783 vs. 0.621). By taking masking risk into consideration our method provides a more comprehensive assessment of breast density.