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Segmentation of Masses on Mammograms Using Data Augmentation and Deep Learning

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Abstract

The diagnosis of breast cancer in early stage is essential for successful treatment. Detection can be performed in several ways, the most common being through mammograms. The projections acquired by this type of examination are directly affected by the composition of the breast, which density can be similar to the suspicious masses, being a challenge the identification of malignant lesions. In this article, we propose a computer-aided detection (CAD) system to aid in the diagnosis of masses in digitized mammograms using a model based in the U-Net, allowing specialists to monitor the lesion over time. Unlike most of the studies, we propose the use of an entire base of digitized mammograms using normal, benign, and malignant cases. Our research is divided into four stages: (1) pre-processing, with the removal of irrelevant information, enhancement of the contrast of 7989 images of the Digital Database for Screening Mammography (DDSM), and obtaining regions of interest. (2) Data augmentation, with horizontal mirroring, zooming, and resizing of images; (3) training, with tests of six-based U-Net models, with different characteristics; (4) testing, evaluating four metrics, accuracy, sensitivity, specificity, and Dice Index. The tested models obtained different results regarding the assessed parameters. The best model achieved a sensitivity of 92.32%, specificity of 80.47%, accuracy of 85.95% Dice Index of 79.39%, and AUC of 86.40%. Even using a full base without case selection bias, the results obtained demonstrate that the use of a complete database can provide knowledge to the CAD expert.

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Notes

  1. http://www.eng.usf.edu/cvprg/Mammogra- phy/Database.html

  2. http://medicalresearch.inescporto.pt/breastresearch/in- dex.php/Get_INbreast_Database

  3. https://bcdr.eu/information/about

  4. https://www.repository.cam.ac.uk/handle/1810/250394

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Authors and Affiliations

  1. Software Innovation Laboratory – SOFTWARELAB, Applied Computing Graduate Program, Universidade do Vale do Rio dos Sinos – Unisinos, Av. Unisinos 950, São Leopoldo, 93022-000, Brazil

    Felipe André Zeiser, Cristiano André da Costa, Tiago Zonta & Rodrigo da Rosa Righi

  2. Ciências da Vida em Pesquisa, Universidade do Oeste de Santa Catarina, Chapecó, Brazil

    Tiago Zonta

  3. Departamento de Informática, Universidade Nova de Lisboa, Almada, Portugal

    Nuno M. C. Marques

  4. Departamento de Patologia e Medicina Legal, Universidade Federal de Ciências da Saúde de, Porto Alegre, Brazil

    Adriana Vial Roehe

  5. Estudos Biológicos e Clínicos em Patologias Humanas, Universidade Federal da Fronteira Sul, Chapecó, Brazil

    Marcelo Moreno

Authors
  1. Felipe André Zeiser
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  2. Cristiano André da Costa
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  7. Rodrigo da Rosa Righi
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Correspondence to Cristiano André da Costa.

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Zeiser, F.A., da Costa, C.A., Zonta, T. et al. Segmentation of Masses on Mammograms Using Data Augmentation and Deep Learning. J Digit Imaging 33, 858–868 (2020). https://doi.org/10.1007/s10278-020-00330-4

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  • DOI: https://doi.org/10.1007/s10278-020-00330-4

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