Segmentation of lung tissue in computed tomography (CT) images is a precursor to most pulmonary image analysis applications. Semantic segmentation methods using deep learning have exhibited top-tier performance in recent years, however designing accurate and robust segmentation models for lung tissue is challenging due to the variations in shape, size, and orientation. Additionally, medical image artifacts and noise can affect lung tissue segmentation and degrade the accuracy of downstream analysis. The practicality of current deep learning methods for lung tissue segmentation is limited as they require significant computational resources and may not be easily deployable in clinical settings. This paper presents a fully automatic method that identifies the lungs in three-dimensional (3D) pulmonary CT images using deep networks and transfer learning. We introduce (1) a novel 2.5-dimensional image representation from consecutive CT slices that succinctly represents volumetric information and (2) a U-Net architecture equipped with pre-trained InceptionV3 blocks to segment 3D CT scans while maintaining the number of learnable parameters as low as possible. Our method was quantitatively assessed using one public dataset, LUNA16, for training and testing and two public datasets, namely, VESSEL12 and CRPF, only for testing. Due to the low number of learnable parameters, our method achieved high generalizability to the unseen VESSEL12 and CRPF datasets while obtaining superior performance over Luna16 compared to existing methods (Dice coefficients of 99.7, 99.1, and 98.8 over LUNA16, VESSEL12, and CRPF datasets, respectively). We made our method publicly accessible via a graphical user interface at medvispy.ee.kntu.ac.ir.

翻译：计算机断层扫描（CT）图像中的肺组织分割是大多数肺部图像分析应用的前置步骤。近年来，基于深度学习的语义分割方法展现出顶尖性能，然而由于肺组织在形状、大小和方向上的变化，设计精确且鲁棒的肺组织分割模型仍面临挑战。此外，医学图像伪影和噪声可能影响肺组织分割，并降低后续分析的准确性。当前深度学习方法在肺组织分割中的实用性受到限制，因为它们需要大量计算资源，且难以在临床环境中便捷部署。本文提出了一种全自动方法，通过深度网络和迁移学习识别三维（3D）肺CT图像中的肺部区域。我们引入了（1）一种基于连续CT切片的新型2.5维图像表示方法，能够简洁地表示体积信息；以及（2）一种配备预训练InceptionV3模块的U-Net架构，用于分割3D CT扫描，同时尽可能保持可学习参数数量较低。我们的方法使用一个公开数据集LUNA16进行训练和测试，并使用两个公开数据集VESSEL12和CRPF仅进行测试，以进行定量评估。由于可学习参数数量较少，我们的方法在未见过的VESSEL12和CRPF数据集上实现了高泛化能力，同时在LUNA16上相较于现有方法获得了更优性能（在LUNA16、VESSEL12和CRPF数据集上的Dice系数分别为99.7、99.1和98.8）。我们通过图形用户界面在medvispy.ee.kntu.ac.ir上公开了该方法。