Dynamical multi-order responses and global semantic-infused adversarial learning: A robust airway segmentation method

Automated airway segmentation in computerized tomography (CT) images is crucial for the accurate diagnosis of lung diseases. However, the scarcity of manual annotations hinders the efficacy of supervised learning, while unconstrained intensities and sample imbalance lead to discontinuity and false-negative issues. To address these challenges, we propose a novel airway segmentation model named Dynamical Multi-order responses and Global Semantic-infused Adversarial network (DMGSA), integrating the unsupervised and supervised learning in parallel to alleviate the label scarcity of airway. In the unsupervised branch, (1) we propose several novel strategies of Dynamic Mask-Ratio (DMR) to empower the model to perceive context information of varying sizes, mimicking the laws of human learning vividly; (2) we present a novel target of Multi-Order Normalized Responses (MONR), exploiting the distinct order exponential operation of raw images and oriented gradients to enhance the textural representations of bronchioles; (3) we introduce the Adversarial Learning (AL) on the top of MONR module to discern nuances between real and fake images, focusing on capturing the textural features of terminal bronchioles. For the supervised branch, we propose an innovative Generalized Mean pooling based Global Semantic-infused (GMGS) module to ulteriorly improve the robustness. Ultimately, we have verified the method performance and robustness by training on normal lung disease datasets, while testing on lung cancer, COVID-19 and Lung fibrosis datasets. All experimental results have proved that our method exceeds state-of-the-art methods significantly.