An end-to-end multi-scale airway segmentation framework based on pulmonary CT image

Objective: Automatic and accurate airway segmentation is necessary for lung disease diagnosis. The complex tree-like structures leads to gaps in the different generations of the airway tree, and thus airway segmentation is also considered to be a multi-scale problem. In recent years, convolutional neural networks have facilitated the development of medical image segmentation. In particular, 2D CNNs and 3D CNNs can extract different scale features. Hence, we propose a two-stage and 2D+3D framework for multi-scale airway tree segmentation. Approach: In stage 1, we use a 2D Full Airway SegNet(2D FA-SegNet) to segment the complete airway tree. Multi-scale Atros Spatial Pyramid (MASP) and Atros Residual Skip connection (ARSc) modules are inserted to extract different scales feature. We designed a hard sample selection strategy to increase the proportion of intrapulmonary airway samples in stage 2. 3D Airway RefineNet (3D ARNet) as stage 2 takes the results of stage 1 as a priori information. Spatial information extracted by 3D convolutional kernel compensates for the loss of in 2D FA-SegNet. Furthermore, we added False Positive losses and False Negative losses to improve the segmentation performance of airway branches within the lungs. Main results: We performed data enhancement on the publicly available dataset of ISICDM 2020 Challenge 3, and on which evaluated our method. Comprehensive experiments show that the proposed method has the highest DSC of 0.931, and IoU of 0.871 for the whole airway tree and DSC of 0.699, and IoU of 0.543 for the intrapulmonary bronchi tree. In addition, 3D ARNet proposed in this paper cascaded with other State-Of-The-Art methods to increase DLR by up to 46.33% and DBR by up to 42.97%. Significance: The quantitative and qualitative evaluation results show that our proposed method performs well in segmenting the airway at different scales.