AirwayNet-SE: A Simple-Yet-Effective Approach to Improve Airway Segmentation Using Context Scale Fusion

Accurate segmentation of airways from chest CT scans is crucial for pulmonary disease diagnosis and surgical navigation. However, the intra-class variety of airways and their intrinsic tree-like structure pose challenges to the development of automatic segmentation methods. Previous work that exploits convolutional neural networks (CNNs) does not take context scales into consideration, leading to performance degradation on peripheral bronchiole. We propose the two-step AirwayNet-SE, a Simple-yet-Effective CNNs-based approach to improve airway segmentation. The first step is to adopt connectivity modeling to transform the binary segmentation task into 26-connectivity prediction task, facilitating the model's comprehension of airway anatomy. The second step is to predict connectivity with a two-stage CNNs-based approach. In the first stage, a Deep-yet-Narrow Network (DNN) and a Shallow-yet-Wide Network (SWN) are respectively utilized to learn features with large-scale and small-scale context knowledge. These two features are fused in the second stage to predict each voxel's probability of being airway and its connectivity relationship between neighbors. We trained our model on 50 CT scans from public datasets and tested on another 20 scans. Compared with state-of-the-art airway segmentation methods, the robustness and superiority of the AirwayNet-SE confirmed the effectiveness of large-scale and small-scale context fusion. In addition, we released our manual airway annotations of 60 CT scans from public datasets for supervised airway segmentation study.