DMRN: A Dynamical Multi-Order Response Network for the Robust Lung Airway Segmentation

Automated airway segmentation in CT images is crucial for lung diseases' diagnosis. However, manual annotation scarcity hinders supervised learning efficacy, while unlimited 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 Response Network (DMRN), 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 enable 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. For the supervised branch, we directly predict the final full segmentation map by the large-ratio cube-masked input instead of full input. Ultimately, we verify 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. Code will be released in the future.