论文标题
通过对合成数据训练的CNN，胸部X光片提取骨结构和增强
Bone Structures Extraction and Enhancement in Chest Radiographs via CNN Trained on Synthetic Data
论文作者
论文摘要
在本文中，我们提出了一种基于学习的图像处理技术，用于使用U-NET FCNN在胸部X光片中提取骨结构。 U-NET经过培训，可以在完全监督的环境中完成任务。为了创建训练图像对，我们采用了模拟的X射线或数字重建X光片（DRR），这些射线照片（DRR）源自属于LIDC-IDRI数据集的664个CT扫描。使用基于HU的CT结构域中的骨结构分割，生产并用于训练网络的合成2D“骨X射线” DRR。对于重建损失，我们使用两个损失功能 - L1损失和感知损失。一旦提取骨结构，可以通过融合原始输入X射线和合成的“骨X射线”来增强原始图像。我们表明，我们的增强技术适用于真实的X射线数据，并在NIH Chest X射线14数据集上显示我们的结果。
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.
