Bone Structures Extraction and Enhancement in Chest Radiographs via CNN Trained on Synthetic Data
This work addresses the challenge of improving bone visibility in chest x-rays for medical imaging applications, but it is incremental as it builds on existing methods with synthetic data.
The paper tackled the problem of extracting and enhancing bone structures in chest radiographs by training a U-Net FCNN on synthetic data from CT scans, achieving results applicable to real x-ray datasets like NIH Chest X-Ray-14.
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.