  Automated segmentation of Lungs plays a crucial role in the computer-aided
diagnosis of chest X-Ray (CXR) images. Developing an efficient Lung
segmentation model is challenging because of difficulties such as the presence
of several edges at the rib cage and clavicle, inconsistent lung shape among
different individuals, and the appearance of the lung apex. In this paper, we
propose a robust model for Lung segmentation in Chest Radiographs. Our model
learns to ignore the irrelevant regions in an input Chest Radiograph while
highlighting regions useful for lung segmentation. The proposed model is
evaluated on two public chest X-Ray datasets (Montgomery County, MD, USA, and
Shenzhen No. 3 People's Hospital in China). The experimental result with a DICE
score of 98.6% demonstrates the robustness of our proposed lung segmentation
approach.
