Airway Segmentation Based on Topological Structure Enhancement Using Multi-task Learning

Xuan Yang, Lingyu Chen, Yuchao Zheng, Longfei Ma, Fang Chen, Guochen Ning, Hongen Liao

Abstract: Airway segmentation in chest computed tomography (CT) images is critical for tracheal disease diagnosis and surgical navigation. However, airway segmentation is challenging due to complex tree structures and branches of different sizes. To enhance airway integrity and reduce fractures during bronchus segmentation, we propose a novel network for airway segmentation, using centerline detection as an auxiliary task to enhance topology awareness. The network introduces a topology embedding interactive module to emphasize the geometric properties of tracheal connections and reduce bronchial breakage. In addition, the proposed topology-enhanced attention module captures contextual and spatial information to improve bronchioles segmentation. In this paper, we conduct qualitative and quantitative experiments on two public datasets. Compared to several state-of-the-art algorithms, our method outperforms in detecting terminal bronchi and ensuring the continuity of the entire trachea while maintaining comparable segmentation accuracy. Our code is available at https://github.com/xyang-11/airway_seg.