@inproceedings{382b189804504cd89994c6976e0f8ecd,
title = "Bone Structures Extraction and Enhancement in Chest Radiographs via CNN Trained on Synthetic Data",
abstract = "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.",
keywords = "CT, DRR, Deep learning, Image enhancement, Image synthesis, X-ray",
author = "Ophir Gozes and Hayit Greenspan",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 17th IEEE International Symposium on Biomedical Imaging, ISBI 2020 ; Conference date: 03-04-2020 Through 07-04-2020",
year = "2020",
month = apr,
doi = "https://doi.org/10.1109/ISBI45749.2020.9098738",
language = "الإنجليزيّة",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "858--861",
booktitle = "ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging",
address = "الولايات المتّحدة",
}