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Coronavirus disease (COVID-19 pneumonia) has spread rapidly and become a global epidemic, which has had a great impact on public health and the economy. The automatic segmentation of lung infections from computed tomography (CT) has become an effective method for diagnosis. In order to realize the full potential of deep learning models in COVID-19 pneumonia infections segmentation, a great deal of annotated CT data is needed for training. The data is difficult to collect due to the high risk of infection, and it is laborious to annotate. Recent advances in image synthesis make it possible to train deep learning models on realistic synthetic data with computer-generated annotations. However, the domain shift between real data and synthetic data significantly reduces segmentation performance. In order to solve this issue, we propose a novel domain adaptation based COVID-19 CT lung infections segmentation network. In this work, we use limited real data without annotations and a large amount of annotated synthetic data to train the U-Net segmentation network jointly. To overcome the domain mismatch, we introduce conditional GAN for adversarial training. We update the segmentation network with the cross-domain adversarial loss. This makes the embedding distribution learned by segmentation network from real data and synthetic data closer, thus greatly improving the representation ability of the segmentation network. The experiment results demonstrate that our proposed network significantly outperforms the baseline and state-of-the-art methods.
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document_parses/pdf_json/40ab6d497179c476c75f5598ee2d129d5e8ecbe6.json
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Domain Adaptation based COVID-19 CT Lung Infections Segmentation Network
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