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.
翻译:3D进化神经网络(CNNs)被广泛采用用于空气路分割。3DCNN的性能受到数据集的极大影响,而公共空气路数据集主要是清洁的CT扫描,有粗略的注解,因此难以推广到吵闹的CT扫描(如COVID-19CT扫描)中。在这项工作中,我们提出了一个新的双流网络,以解决清洁域与噪音域之间的变异性,利用清洁的CT扫描和少量贴有标签的噪音CT对空气路分割进行扫描。我们设计了两个不同的编码器,分别提取可转移的清洁特性和独特的噪音特性,随后是两个独立的解析器。此外,可转移特性通过频道特性重新校正和连接距离图(SDM)回归加以改进。功能校正模块强调关键特性,SDM对溴域给予更多的注意,这有助于提取可转移的表层特征,使之与粗糙标签相容。我们提出的系统扫描方法的清晰度改进了我们所提议的方法。