  Lung segmentation in computerized tomography (CT) images is an important
procedure in various lung disease diagnosis. Most of the current lung
segmentation approaches are performed through a series of procedures with
manually empirical parameter adjustments in each step. Pursuing an automatic
segmentation method with fewer steps, in this paper, we propose a novel deep
learning Generative Adversarial Network (GAN) based lung segmentation schema,
which we denote as LGAN. Our proposed schema can be generalized to different
kinds of neural networks for lung segmentation in CT images and is evaluated on
a dataset containing 220 individual CT scans with two metrics: segmentation
quality and shape similarity. Also, we compared our work with current state of
the art methods. The results obtained with this study demonstrate that the
proposed LGAN schema can be used as a promising tool for automatic lung
segmentation due to its simplified procedure as well as its good performance.
