  This paper proposes a novel framework for lung segmentation in chest X-rays.
It consists of two key contributions, a criss-cross attention based
segmentation network and radiorealistic chest X-ray image synthesis (i.e. a
synthesized radiograph that appears anatomically realistic) for data
augmentation. The criss-cross attention modules capture rich global contextual
information in both horizontal and vertical directions for all the pixels thus
facilitating accurate lung segmentation. To reduce the manual annotation burden
and to train a robust lung segmentor that can be adapted to pathological lungs
with hazy lung boundaries, an image-to-image translation module is employed to
synthesize radiorealistic abnormal CXRs from the source of normal ones for data
augmentation. The lung masks of synthetic abnormal CXRs are propagated from the
segmentation results of their normal counterparts, and then serve as pseudo
masks for robust segmentor training. In addition, we annotate 100 CXRs with
lung masks on a more challenging NIH Chest X-ray dataset containing both
posterioranterior and anteroposterior views for evaluation. Extensive
experiments validate the robustness and effectiveness of the proposed
framework. The code and data can be found from
https://github.com/rsummers11/CADLab/tree/master/Lung_Segmentation_XLSor .
