  In this paper, we present a deep learning-based image processing technique
for extraction of bone structures in chest radiographs using a U-Net FCNN. The
U-Net was trained to accomplish the task in a fully supervised setting. To
create the training image pairs, we employed simulated X-Ray or Digitally
Reconstructed Radiographs (DRR), derived from 664 CT scans belonging to the
LIDC-IDRI dataset. Using HU based segmentation of bone structures in the CT
domain, a synthetic 2D "Bone x-ray" DRR is produced and used for training the
network. For the reconstruction loss, we utilize two loss functions- L1 Loss
and perceptual loss. Once the bone structures are extracted, the original image
can be enhanced by fusing the original input x-ray and the synthesized "Bone
X-ray". We show that our enhancement technique is applicable to real x-ray
data, and display our results on the NIH Chest X-Ray-14 dataset.
