论文标题
注意胸部X射线肺分段的基于U-NET的对抗体系结构
Attention U-Net Based Adversarial Architectures for Chest X-ray Lung Segmentation
论文作者
论文摘要
胸部X射线是医学成像方式中最常见的测试。它用于检测和分化，除其他外，肺癌，结核病和肺炎是由于Covid-19疾病而引起的最后一次重要性。将计算机辅助检测方法集成到放射科医生诊断管道中，大大降低了医生的工作量，增加了可靠性和定量分析。在这里，我们提出了一种新颖的深度学习方法，用于肺部分割，这是诊断管道中的基本但艰巨的任务。我们的方法与对抗性评论家模型一起使用了最先进的完全卷积神经网络。它概括地概括为具有不同患者概况的看不见数据集的CXR图像，在JSRT数据集中达到了97.5％的最终DSC。
Chest X-ray is the most common test among medical imaging modalities. It is applied for detection and differentiation of, among others, lung cancer, tuberculosis, and pneumonia, the last with importance due to the COVID-19 disease. Integrating computer-aided detection methods into the radiologist diagnostic pipeline, greatly reduces the doctors' workload, increasing reliability and quantitative analysis. Here we present a novel deep learning approach for lung segmentation, a basic, but arduous task in the diagnostic pipeline. Our method uses state-of-the-art fully convolutional neural networks in conjunction with an adversarial critic model. It generalized well to CXR images of unseen datasets with different patient profiles, achieving a final DSC of 97.5% on the JSRT dataset.
