Direction-Aware convolution for airway tubular feature enhancement network.

Manual annotation of airway regions in computed tomography images is a time-consuming and expertise-dependent task. Automatic airway segmentation is therefore a prerequisite for enabling rapid bronchoscopic navigation and the clinical deployment of bronchoscopic robotic systems. Although convolutional neural network methods have gained considerable attention in airway segmentation, the unique tree-like structure of airways poses challenges for conventional and deformable convolutions, which often fail to focus on fine airway structures, leading to missed segments and discontinuities. To address this issue, this study proposes a novel tubular feature extraction network, named TfeNet. TfeNet introduces a novel direction-aware convolution operator that adapts the geometry of linear convolution kernels through spatial rotation transformations, enabling it to dynamically align with the tubular structures of airways and effectively enhance feature extraction. Furthermore, a tubular feature fusion module (TFFM) is designed based on asymmetric convolution and residual connection strategies, effectively capturing the features of airway tubules from different directions. Extensive experiments conducted on one public dataset and two datasets used in airway segmentation challenges demonstrate the effectiveness of TfeNet. Specifically, our method achieves a comprehensive lead in both accuracy and continuity on the BAS dataset, attains the highest mean score of 94.95 % on the ATM22 dataset by balancing accuracy and continuity, and demonstrates superior leakage control and precision on the challenging AIIB23 dataset. The code is available at https://github.com/QibiaoWu/TfeNet.