X-ray is by far the most common among medical imaging modalities, being faster, more accessible, and more cost-effective compared to other radiographic methods. Chest X-ray (CXR) is the most commonly requested test due to its contribution to the early detection of lung cancer. The most important biomarker in detecting cancer of the lung are nodules, and in finding those, lung segmentation of chest X-rays is essential. Another goal is interpretability, helping radiologists integrate

more »

... d detection methods into their diagnostic pipeline, greatly reducing their workload. For this reason, a robust algorithm to perform this otherwise arduous segmentation task is much desired in the field of medical imaging. In this work, we present a novel deep learning approach that uses stateof-the-art fully convolutional neural networks in conjunction with an adversarial critic model. Our network generalized well to CXR images of unseen datasets with different patient profiles, achieving a final DSC of 97.5% on the JSRT CXR dataset.