Exploring Deep-Based Approaches for Semantic Segmentation of Mammographic Images

  • Hugo Neves de OliveiraEmail author
  • Claudio Saliba de Avelar
  • Alexei Manso Corrêa Machado
  • Arnaldo de Albuquerque Araujo
  • Jefersson Alex dos Santos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)


Pectoral muscle and background elimination are common steps for automated software in mammographic image preprocessing. We investigate FCNs, U-nets and SegNets in the task of mammogram segmentation, addressing three subtasks: pectoral muscle, background and breast region segmentation. The MIAS and INbreast datasets were used for evaluating Deep Neural Networks on the segmentation of these regions. Several objective evaluation metrics were used in order to compare our results with the ones available in the literature. State-of-the-art results were observed in most comparisons, significantly surpassing the baselines in most metrics. Best Jaccard values (in %) for Deep Learning algorithms were \(89.7\pm 2.5\), \(98.4\pm 0.1\) and \(97.0\pm 0.4\) for pectoral muscle, background and breast region segmentation, respectively, in the MIAS dataset. For INbreast, the best Jaccard value achieved for pectoral muscle segmentation was \(90.8\pm 2.5\).


Pectoral muscle segmentation Breast segmentation Mammography Deep Learning 



Authors would like to thank NVIDIA for their support with GPUs; CAPES, CNPq and FAPEMIG (APQ-00449-17) for their financial support and Drs. Andrik Rampun and Arnau Oliver for the MIAS ground truths.


  1. 1.
    Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. TPAMI 39(12), 2481–2495 (2017)CrossRefGoogle Scholar
  2. 2.
    Camilus, K.S., Govindan, V., Sathidevi, P.: Pectoral muscle identification in mammograms. J. Appl. Clin. Med. Phys. 12(3), 215–230 (2011)CrossRefGoogle Scholar
  3. 3.
    Ferrari, R., Frere, A., Rangayyan, R., Desautels, J., Borges, R.: Identification of the breast boundary in mammograms using active contour models. Med. Biol. Eng. Comput. 42(2), 201–208 (2004)CrossRefGoogle Scholar
  4. 4.
    Ganesan, K., Acharya, U.R., Chua, K.C., Min, L.C., Abraham, K.T.: Pectoral muscle segmentation: a review. CMPB 110(1), 48–57 (2013)Google Scholar
  5. 5.
    Heath, M., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer, P.: The digital database for screening mammography. In: Digital Mammography, pp. 431–434 (2000)Google Scholar
  6. 6.
    Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. In: CVPR, vol. 1, p. 3 (2017)Google Scholar
  7. 7.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) NIPS, pp. 1097–1105. Curran Associates, Inc., New York (2012)Google Scholar
  8. 8.
    Li, Y., Chen, H., Yang, Y., Yang, N.: Pectoral muscle segmentation in mammograms based on homogenous texture and intensity deviation. Pattern Recogn. 46(3), 681–691 (2013)CrossRefGoogle Scholar
  9. 9.
    Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)CrossRefGoogle Scholar
  10. 10.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440, June 2015Google Scholar
  11. 11.
    Lopez, M.G., et al.: BCDR: a breast cancer digital repository. In: ICEM (2012)Google Scholar
  12. 12.
    Moreira, I.C., Amaral, I., Domingues, I., Cardoso, A., Cardoso, M.J., Cardoso, J.S.: INbreast: toward a full-field digital mammographic database. Acad. Radiol. 19(2), 236–248 (2012)CrossRefGoogle Scholar
  13. 13.
    Oliver, A., Lladó, X., Torrent, A., Martí, J.: One-shot segmentation of breast, pectoral muscle, and background in digitised mammograms. In: ICIP (2014)Google Scholar
  14. 14.
    Rampun, A., Morrow, P.J., Scotney, B.W., Winder, J.: Fully automated breast boundary and pectoral muscle segmentation in mammograms. Artif. Intell. Med. 79, 28–41 (2017)CrossRefGoogle Scholar
  15. 15.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). Scholar
  16. 16.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  17. 17.
    Suckling, J., et al.: The mammographic image analysis society digital mammogram database. In: Exerpta Medica. International Congress Series, vol. 1069, pp. 375–378 (1994)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hugo Neves de Oliveira
    • 1
    Email author
  • Claudio Saliba de Avelar
    • 2
  • Alexei Manso Corrêa Machado
    • 3
    • 4
  • Arnaldo de Albuquerque Araujo
    • 1
  • Jefersson Alex dos Santos
    • 1
  1. 1.Computer Science DepartmentUniversidade Federal de Minas GeraisBelo HorizonteBrazil
  2. 2.Clinical Hospital, Universidade Federal de Minas GeraisBelo HorizonteBrazil
  3. 3.School of MedicineUniversidade Federal de Minas GeraisBelo HorizonteBrazil
  4. 4.Computer Science DepartmentPUC MinasBelo HorizonteBrazil

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