  Chest X-ray (CXR) is one of the most commonly prescribed medical imaging
procedures, often with over 2-10x more scans than other imaging modalities such
as MRI, CT scan, and PET scans. These voluminous CXR scans place significant
workloads on radiologists and medical practitioners. Organ segmentation is a
crucial step to obtain effective computer-aided detection on CXR. In this work,
we propose Structure Correcting Adversarial Network (SCAN) to segment lung
fields and the heart in CXR images. SCAN incorporates a critic network to
impose on the convolutional segmentation network the structural regularities
emerging from human physiology. During training, the critic network learns to
discriminate between the ground truth organ annotations from the masks
synthesized by the segmentation network. Through this adversarial process the
critic network learns the higher order structures and guides the segmentation
model to achieve realistic segmentation outcomes. Extensive experiments show
that our method produces highly accurate and natural segmentation. Using only
very limited training data available, our model reaches human-level performance
without relying on any existing trained model or dataset. Our method also
generalizes well to CXR images from a different patient population and disease
profiles, surpassing the current state-of-the-art.
