To improve the early diagnosis and treatment of lung diseases automated lung segmentation from CT images is a crucial task for clinical decision. The segmentation of the lung region from the CT scans is a very challenging task due to the irregular shape and size of lungs, low contrast and fuzzy boundaries of the lung. The manual segmentation of lung CT images is a laborious task. Therefore, various approaches are suggested by the researcher in the recent past for the automated lung segmentation. However, the existing approaches either utilize low-level handcraft features or CNN based Fully Convolutional Networks. The low-level hand-craft feature-based approaches lead to poor generalization, while the shallower networks are unable to extract more discriminative features. Hence, in this study, we have implemented a deep learning-based architecture called Residual U-Net with a false-positive removal algorithm for lung CT segmentation. Here, we have suggested that learning from a substantially deeper network with residual units can extract more discriminative feature representation as compared to shallow network for lung segmentation. To take full advantage of the deeper network, we have utilized a set of schemes to ensure efficient training. First, we have implemented a U-Net architecture with residual block to overcome the problem of performance degradation. Further, various data augmentation techniques are utilized to improve the generalization capability of the proposed method. The experimental results show that the proposed method achieved competitive results over the existing methods with DSC of 98.63%, 99.62% and 98.68% for LUNA16, VESSEL12 and HUG-ILD dataset respectively.

为了改善肺部疾病的早期诊断和治疗，从CT图像进行自动肺分割是临床决策的关键任务。由于肺部形状和大小不规则，肺部对比度低和边界模糊，因此通过CT扫描对肺区域进行分割是一项非常具有挑战性的任务。手动分割肺部CT图像是一项艰巨的任务。因此，研究人员在最近提出了各种用于自动肺分割的方法。但是，现有方法要么利用低级手工功能，要么利用基于CNN的全卷积网络。低级的基于手工艺特征的方法导致较差的泛化能力，而较浅的网络无法提取更多的歧视性特征。因此，在这项研究中 我们已经实现了一种基于深度学习的架构，称为Residual U-Net，具有用于肺部CT分割的误报去除算法。在这里，我们已经建议，与用于肺分割的浅层网络相比，从具有残留单元的实质上更深的网络中学习可以提取出更多的判别性特征表示。为了充分利用更深层次的网络，我们使用了一套方案来确保有效的培训。首先，我们实现了带有残留块的U-Net架构，以克服性能下降的问题。此外，利用各种数据增强技术来提高所提出方法的泛化能力。实验结果表明，与LUNA16的DSC分别为98.63％，99.62％和98.68％相比，该方法取得了优于现有方法的竞争结果，