A DCT-UNet-based framework for pulmonary airway segmentation integrating label self-updating and terminal region growing

Abstract Objective. Intrathoracic airway segmentation in computed tomography is important for quantitative and qualitative analysis of various chronic respiratory diseases and bronchial surgery navigation. However, the airway tree’s morphological complexity, incomplete labels resulting from annotation difficulty, and intra-class imbalance between main and terminal airways limit the segmentation performance. Approach. Three methodological improvements are proposed to deal with the challenges. Firstly, we design a dilated contextual transformer-UNet to collect better information on neighboring voxels and ones within a larger spatial region. Secondly, an airway label self-updating strategy is proposed to iteratively update the reference labels to conquer the problem of incomplete labels. Thirdly, a deep learning-based terminal region growing is adopted to extract terminal airways. Extensive experiments were conducted on two internal datasets and three public datasets. Main Results. Compared to the counterparts, the proposed method can achieve a higher branch detected, tree-length detected, branch ratio, and tree-length ratio (ISICDM2021 dataset, 95.19%, 94.89%, 166.45%, and 172.29%; binary airway segmentation dataset, 96.03%, 95.11%, 129.35%, and 137.00%). Ablation experiments show the effectiveness of three proposed solutions. Our method is applied to an in-house chronic obstructive pulmonary disease (COPD) dataset. The measures of branch count, tree length, endpoint count, airway volume, and airway surface area are significantly different between COPD severity stages. Significance. The proposed methods can segment more terminal bronchi and larger length of airway, even some bronchi which are real but missed in the manual annotation can be detected. Potential application significance has been presented in characterizing COPD airway lesions and severity stages.