Segmentation of Masses on Mammograms Using Data Augmentation and Deep Learning
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ORIGINAL PAPER
Segmentation of Masses on Mammograms Using Data Augmentation and Deep Learning Felipe André Zeiser 1 & Cristiano André da Costa 1 Marcelo Moreno 5 & Rodrigo da Rosa Righi 1
&
Tiago Zonta 1,2 & Nuno M. C. Marques 3 & Adriana Vial Roehe 4 &
# Society for Imaging Informatics in Medicine 2020
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. Keywords Breast cancer . Segmentation . Computer-aided detection . Fully convolutional network . Deep learning . U-Net
Introduction Breast cancer is the most deadly disease in women, accounting for more than half a million deaths per year [3]. It is the second most incident type of cancer in the world [3]. Even in
the face of advances in treatment, early diagnosis is a crucial point for defining the patient outcome. Currently, the primary technique used in screening programs for breast cancer is the mammogram [8]. Traditionally, two projections per breast are obtained, one
* Cristiano André da Costa [email protected]
Rodrigo da Rosa Righi [email protected] 1
Tiago Zonta [email protected]
Software Innovation Laboratory – SOFTWARELAB, Applied Computing Graduate Program, Universidade do Vale do Rio dos Sinos – Unisinos, Av. Unisinos 950, São Leopoldo 93022-000, Brazil
2
Ciências da Vida em Pesquisa, Universidade do Oeste de Santa Catarina, Chapecó, Brazil
Nuno M. C. Marques [email protected]
3
Departamento de Informática, Universidade Nova de Lisboa, Almada, Portugal
Adriana Vial Roehe [email protected]
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