  Chest X-ray (CXR) is a low-cost medical imaging technique. It is a common
procedure for the identification of many respiratory diseases compared to MRI,
CT, and PET scans. This paper presents the use of generative adversarial
networks (GAN) to perform the task of lung segmentation on a given CXR. GANs
are popular to generate realistic data by learning the mapping from one domain
to another. In our work, the generator of the GAN is trained to generate a
segmented mask of a given input CXR. The discriminator distinguishes between a
ground truth and the generated mask, and updates the generator through the
adversarial loss measure. The objective is to generate masks for the input CXR,
which are as realistic as possible compared to the ground truth masks. The
model is trained and evaluated using four different discriminators referred to
as D1, D2, D3, and D4, respectively. Experimental results on three different
CXR datasets reveal that the proposed model is able to achieve a dice-score of
0.9740, and IOU score of 0.943, which are better than other reported
state-of-the art results.
