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:胸部计算机断层扫描(CT)图像中的肺部组织分割是大多数肺部图像分析应用的前提条件。近年来,使用深度学习的语义分割方法在肺部组织分割方面展示了高水平的性能,然而,由于形状、大小和方向的变化,设计精确而稳健的肺部分割模型具有挑战性。此外,医学图像伪影和噪声可能会影响肺部组织分割并降低下游分析的精度。目前的深度学习方法对于肺部组织分割的实用性受到限制,因为它们需要大量的计算资源,并且可能不容易在临床环境中部署。本文介绍一种完全自动的方法,该方法使用深度网络和迁移学习在三维(3D)肺部CT图像中识别肺部。我们引入了(1)从连续的CT切片中获取的新的2.5维图像表示,该表示简洁地表示了体积信息和(2)U-Net架构,该架构配备有预训练的InceptionV3块,以在保持可学习参数数量尽可能低的情况下对3D CT扫描进行分割。我们使用一个公共数据集LUNA16进行了定量评估,用于训练和测试两个公共数据集,即VESSEL12和CRPF,仅用于测试。由于可学习参数数量较少,我们的方法在未看见的VESSEL12和CRPF数据集上实现了高可泛化性,同时在Luna16上相对于现有方法获得了优越的性能(分别在LUNA16、VESSEL12和CRPF数据集上的Dice系数为99.7、99.1和98.8)。我们通过medvispy.ee.kntu.ac.ir提供了我们的方法的公共访问。