  Mammogram inspection in search of breast tumors is a tough assignment that
radiologists must carry out frequently. Therefore, image analysis methods are
needed for the detection and delineation of breast masses, which portray
crucial morphological information that will support reliable diagnosis. In this
paper, we proposed a conditional Generative Adversarial Network (cGAN) devised
to segment a breast mass within a region of interest (ROI) in a mammogram. The
generative network learns to recognize the breast mass area and to create the
binary mask that outlines the breast mass. In turn, the adversarial network
learns to distinguish between real (ground truth) and synthetic segmentations,
thus enforcing the generative network to create binary masks as realistic as
possible. The cGAN works well even when the number of training samples are
limited. Therefore, the proposed method outperforms several state-of-the-art
approaches. This hypothesis is corroborated by diverse experiments performed on
two datasets, the public INbreast and a private in-house dataset. The proposed
segmentation model provides a high Dice coefficient and Intersection over Union
(IoU) of 94% and 87%, respectively. In addition, a shape descriptor based on a
Convolutional Neural Network (CNN) is proposed to classify the generated masks
into four mass shapes: irregular, lobular, oval and round. The proposed shape
descriptor was trained on Digital Database for Screening Mammography (DDSM)
yielding an overall accuracy of 80%, which outperforms the current
state-of-the-art.
