Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Nov 23;10(11):988.
doi: 10.3390/diagnostics10110988.

Fully Automated Breast Density Segmentation and Classification Using Deep Learning

Nasibeh Saffari  1 , Hatem A Rashwan  1 , Mohamed Abdel-Nasser  1   2 , Vivek Kumar Singh  1 , Meritxell Arenas  3 , Eleni Mangina  4 , Blas Herrera  1 , Domenec Puig  1
Affiliations

Fully Automated Breast Density Segmentation and Classification Using Deep Learning

Nasibeh Saffari et al. Diagnostics (Basel). .

Abstract

Breast density estimation with visual evaluation is still challenging due to low contrast and significant fluctuations in the mammograms' fatty tissue background. The primary key to breast density classification is to detect the dense tissues in the mammographic images correctly. Many methods have been proposed for breast density estimation; nevertheless, most of them are not fully automated. Besides, they have been badly affected by low signal-to-noise ratio and variability of density in appearance and texture. This study intends to develop a fully automated and digitalized breast tissue segmentation and classification using advanced deep learning techniques. The conditional Generative Adversarial Networks (cGAN) network is applied to segment the dense tissues in mammograms. To have a complete system for breast density classification, we propose a Convolutional Neural Network (CNN) to classify mammograms based on the standardization of Breast Imaging-Reporting and Data System (BI-RADS). The classification network is fed by the segmented masks of dense tissues generated by the cGAN network. For screening mammography, 410 images of 115 patients from the INbreast dataset were used. The proposed framework can segment the dense regions with an accuracy, Dice coefficient, Jaccard index of 98%, 88%, and 78%, respectively. Furthermore, we obtained precision, sensitivity, and specificity of 97.85%, 97.85%, and 99.28%, respectively, for breast density classification. This study's findings are promising and show that the proposed deep learning-based techniques can produce a clinically useful computer-aided tool for breast density analysis by digital mammography.

Keywords: breast cancer; breast density; convolutional neural network; deep learning; generative adversarial networks; mammograms.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Prevalence, relative risks of developing breast cancer based on Four classes of Breast Imaging and Reporting Data ystem (BI-RADS) density standard (i.e., fatty, scattered fibroglandular density, heterogeneously dense, and extremely dense).
Figure 2
Figure 2
An overview of proposed framework.
Figure 3
Figure 3
(a) Original images. (b) After removing pectoral muscle.
Figure 4
Figure 4
cGAN framework for segmentation.
Figure 5
Figure 5
cGAN framework for breast density segmentation.
Figure 6
Figure 6
Proposed CNN architecture used for breast density classification (second technique).
Figure 7
Figure 7
Breast density segmentation result of three models with the INbreast dataset. (Row 1) original images. (Row 2) Ground truth. (Row 3) Result of cGAN-UNet with dice-loss. (Row 4) Result of cGAN-UNet with myssim loss. (Row 5) Result of cGAN-UNet. (Col 1) BI-RADS I from two different views (CC and MLO). (Col 2) BI-RADS II. (Col 3) BI-RADS III. (Col 4) BI-RADS IV.
See this image and copyright information in PMC

References

    1. Abdel-Nasser M., Rashwan H.A., Puig D., Moreno A. Analysis of tissue abnormality and breast density in mammographic images using a uniform local directional pattern. Expert Syst. Appl. 2015;42:9499–9511.
    1. Abbas Q. DeepCAD: A computer-aided diagnosis system for mammographic masses using deep invariant features. Computers. 2016;5:28.
    1. Astley S.M., Harkness E.F., Sergeant J.C., Warwick J., Stavrinos P., Warren R., Wilson M., Beetles U., Gadde S., Lim Y., et al. A comparison of five methods of measuring mammographic density: A case-control study. Breast Cancer Res. 2018;20:10. - PMC - PubMed
    1. Keller B.M., Nathan D.L., Wang Y., Zheng Y., Gee J.C., Conant E.F., Kontos D. Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation. Med. Phys. 2012;39:4903–4917. - PMC - PubMed
    1. Sprague B.L., Stout N.K., Schechter C., Van Ravesteyn N.T., Cevik M., Alagoz O., Lee C.I., Van Den Broek J.J., Miglioretti D.L., Mandelblatt J.S., et al. Benefits, harms, and cost-effectiveness of supplemental ultrasonography screening for women with dense breasts. Ann. Intern. Med. 2015;162:157–166. - PMC - PubMed

LinkOut - more resources