Accurate airway extraction from computed tomography (CT) images is a critical step for planning navigation bronchoscopy and quantitative assessment of airway-related chronic obstructive pulmonary disease (COPD). The existing methods are challenging to sufficiently segment the airway, especially the high-generation airway, with the constraint of the limited label and cannot meet the clinical use in COPD. We propose a novel two-stage 3D contextual transformer-based U-Net for airway segmentation using CT images. The method consists of two stages, performing initial and refined airway segmentation. The two-stage model shares the same subnetwork with different airway masks as input. Contextual transformer block is performed both in the encoder and decoder path of the subnetwork to finish high-quality airway segmentation effectively. In the first stage, the total airway mask and CT images are provided to the subnetwork, and the intrapulmonary airway mask and corresponding CT scans to the subnetwork in the second stage. Then the predictions of the two-stage method are merged as the final prediction. Extensive experiments were performed on in-house and multiple public datasets. Quantitative and qualitative analysis demonstrate that our proposed method extracted much more branches and lengths of the tree while accomplishing state-of-the-art airway segmentation performance. The code is available at https://github.com/zhaozsq/airway_segmentation.
翻译:从计算断层成像(CT)图像中精密的空气路提取,是规划导航支气管检查和对与空气有关的慢性阻塞性肺病进行定量评估的关键步骤。现有方法对于在有限标签的限制下充分分割空气路,特别是高代式空气路具有挑战性,无法满足COPD的临床用途。我们提议使用CT图像进行双阶段三维背景变压器U-Net的空气路分割。方法由两个阶段组成,进行初步和改良的空气路分割。两阶段模型与不同的气道遮罩共享同一个子网络,投入中不同的空气路面。亚背景变异器块在子网络的编码和解码路径上进行,以有效完成高质量的空气路分割。在第一阶段,向子网络提供总气道遮罩和CT图像,在磁带的空气路遮罩和相应的CT扫描到子网络的第二个阶段。随后,两阶段方法的预测与不同的子网络共享同一个子网络,作为输入输入的气道面面面面面面面面面面面面面面面面面。在最后预测中进行广泛的测试,并在州一级进行更多的空气路段分析,在州级/直径分析中进行大量分析。在州内部分析中进行。