  3D Convolutional Neural Networks (CNNs) have been widely adopted for airway
segmentation. The performance of 3D CNNs is greatly influenced by the dataset
while the public airway datasets are mainly clean CT scans with coarse
annotation, thus difficult to be generalized to noisy CT scans (e.g. COVID-19
CT scans). In this work, we proposed a new dual-stream network to address the
variability between the clean domain and noisy domain, which utilizes the clean
CT scans and a small amount of labeled noisy CT scans for airway segmentation.
We designed two different encoders to extract the transferable clean features
and the unique noisy features separately, followed by two independent decoders.
Further on, the transferable features are refined by the channel-wise feature
recalibration and Signed Distance Map (SDM) regression. The feature
recalibration module emphasizes critical features and the SDM pays more
attention to the bronchi, which is beneficial to extracting the transferable
topological features robust to the coarse labels. Extensive experimental
results demonstrated the obvious improvement brought by our proposed method.
Compared to other state-of-the-art transfer learning methods, our method
accurately segmented more bronchi in the noisy CT scans.
