ABSTRACT
Detailed modeling of the airway tree from CT scan is important for 3D navigation involved in endobronchial intervention including for those patients infected with the novel coronavirus. Deep learning methods have the potential for automatic airway segmentation but require large annotated datasets for training, which is difficult for a small patient population and rare cases. Due to the unique attributes of noisy COVID-19 CTs (e.g., ground-glass opacity and consolidation), vanilla 3D Convolutional Neural Networks (CNNs) trained on clean CTs are difficult to be generalized to noisy CTs. In this work, a Collaborative Feature Disentanglement and Augmentation framework (CFDA) is proposed to harness the intrinsic topological knowledge of the airway tree from clean CTs incorporated with unique bias features extracted from the noisy CTs. Firstly, we utilize the clean CT scans and a small amount of labeled noisy CT scans to jointly acquire a bias-discriminative encoder. Feature-level augmentation is then designed to perform feature sharing and augmentation, which diversifies the training samples and increases the generalization ability. Detailed evaluation results on patient datasets demonstrated considerable improvements in the CFDA network. It has been shown that the proposed method achieves superior segmentation performance of airway in COVID-19 CTs against other state-of-the-art transfer learning methods. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.