Abstract
The radiographic appearance of breast tissue has been established as a strong risk factor for breast cancer. Here we present a complete machine learning framework for automatic estimation of mammographic density (MD) and robust feature extraction for breast cancer risk analysis. Our framework is able to simultaneously classify the breast region, fatty tissue, pectoral muscle, glandular tissue and nipple region. Integral to our method is the extraction of measures of breast density (as the fraction of the breast area occupied by glandular tissue) and mammographic pattern. A novel aspect of the segmentation framework is that a probability map associated with the label mask is provided, which indicates the level of confidence of each pixel being classified as the current label. The Pearson correlation coefficient between the estimated MD value and the ground truth is 0.8012 (p-value<0.0001). We demonstrate the capability of our methods to discriminate between women with and without cancer by analyzing the contralateral mammograms of 50 women with unilateral breast cancer, and 50 controls. Using MD we obtained an area under the ROC curve (AUC) of 0.61; however our texture-based measure of mammographic pattern significantly outperforms the MD discrimination with an AUC of 0.70.
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McCormack, V., dos, I., Silva, S.: Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol. Biomarkers Prev. 15(6), 1159–1169 (2006)
Boyd, N., Martin, L., Bronskill, M., Yaffe, M., Duric, N., Minkin, S.: Breast tissue composition and susceptibility to breast cancer. J. Natl. Cancer Inst. 102(16), 1224–1237 (2010)
Byng, J., Boyd, N., Fishell, E., Jong, R., Yaffe, M.: The quantitative analysis of mammographic densities. Phys. Med. Biol. 39(10), 1629 (1994)
Keller, B., Nathan, D., Wang, Y., Zheng, Y., Gee, J., Conant, E., 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. Phys. Med. Biol. 39(8), 4903–4917 (2012)
Alonzo-Proulx, O., Packard, N., Boone, J., Al-Mayah, A., Brock, K., Shen, S., Yaffe, M.: Validation of a method for measuring the volumetric breast density from digital mammograms. Phys. Med. Biol. 55(11), 3027–3044 (2010)
Jeffreys, M., Harvey, J., Highnam, R.: Comparing a New Volumetric Breast Density Method (VolparaTM) to Cumulus. In: Martí, J., Oliver, A., Freixenet, J., Martí, R. (eds.) IWDM 2010. LNCS, vol. 6136, pp. 408–413. Springer, Heidelberg (2010)
Wolfe, J.: Breast patterns as an index of risk for developing breast cancer. Am. J. Roentgenol. 126(6), 1130–1137 (1976)
Gram, I., Funkhouser, E., Tabár, L.: “The Tabár classification of mammographic parenchymal patterns ”. Eur. J. Radiol. 24(2), 131–136 (1997)
Nielsen, M., Karemore, G., Loog, M., Raundahl, J., Karssemeijer, N., Otten, J., Karsdal, M., Vachon, C., Christiansen, C.: A novel and automatic mammographic texture resemblance marker is an independent risk factor for breast cancer. Cancer Epidemiol. 35(4), 381–387 (2011)
Chen, X., Moschidis, E., Taylor, C., Astley, S.: A novel framework for fat, glandular tissue, pectoral muscle and nipple segmentation in full field digital mammograms. In: Fujita, H., Hara, T., Muramatsu, C. (eds.) IWDM 2014. LNCS, vol. 8539, pp. 201–208. Springer, Heidelberg (2014)
Berks, M., Chen, Z., Astley, S., Taylor, C.: Detecting and classifying linear structures in mammograms using random forests. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 510–524. Springer, Heidelberg (2011)
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Chen, X., Moschidis, E., Taylor, C., Astley, S. (2014). Breast Cancer Risk Analysis Based on a Novel Segmentation Framework for Digital Mammograms. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8673. Springer, Cham. https://doi.org/10.1007/978-3-319-10404-1_67
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DOI: https://doi.org/10.1007/978-3-319-10404-1_67
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