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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)

Abstract

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 \(90.8\pm 2.5\).

Keywords

Pectoral muscle segmentation Breast segmentation Mammography Deep Learning 

Notes

Acknowledgments

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.

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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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