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

FDA: Feature Decomposition and Aggregation for Robust Airway Segmentation