The City University of New York, New York 10016, USA.
Memorial Sloan Kettering Cancer Center, New York 10065, USA.
Comput Med Imaging Graph. 2021 Jan;87:101817. doi: 10.1016/j.compmedimag.2020.101817. Epub 2020 Nov 16.
Lung segmentation in Computerized Tomography (CT) images plays an important role in various lung disease diagnosis. Most of the current lung segmentation approaches are performed through a series of procedures with manually empirical parameter adjustments in each step. Pursuing an automatic segmentation method with fewer steps, we propose a novel deep learning Generative Adversarial Network (GAN)-based lung segmentation schema, which we denote as LGAN. The proposed schema can be generalized to different kinds of neural networks for lung segmentation in CT images. We evaluated the proposed LGAN schema on datasets including Lung Image Database Consortium image collection (LIDC-IDRI) and Quantitative Imaging Network (QIN) collection with two metrics: segmentation quality and shape similarity. Also, we compared our work with current state-of-the-art methods. The experimental results demonstrated that the proposed LGAN schema can be used as a promising tool for automatic lung segmentation due to its simplified procedure as well as its improved performance and efficiency.
计算机断层扫描（CT）图像中的肺分割在各种肺病诊断中起着重要作用。目前大多数的肺分割方法都是通过一系列步骤来实现的，每个步骤都需要手动经验参数调整。为了追求更简单的自动分割方法，我们提出了一种基于深度生成式对抗网络（GAN）的新型肺分割方案，我们称之为 LGAN。该方案可以推广到不同种类的神经网络，用于 CT 图像中的肺分割。我们使用两个指标，即分割质量和形状相似度，在包括 Lung Image Database Consortium 图像集（LIDC-IDRI）和 Quantitative Imaging Network（QIN）集在内的数据集上评估了所提出的 LGAN 方案，同时还将我们的工作与当前最先进的方法进行了比较。实验结果表明，由于其简化的步骤以及改进的性能和效率，所提出的 LGAN 方案可以作为一种很有前途的自动肺分割工具。
