对于管状物体敏感且应用于肺气道与动脉静脉分割的卷积神经网络结构

Learning Tubule-Sensitive CNNs for Pulmonary Airway and Artery-Vein Segmentation in CT

论文链接（paper link）： https://arxiv.org/abs/2012.05767

论文摘要（Abstract）：

Training convolutional neural networks (CNNs) for segmentation of pulmonary airway, artery, and vein is challenging due to sparse supervisory signals caused by the severe class imbalance between tubular targets and background. We present a CNNs-based method for accurate airway and artery-vein segmentation in non-contrast computed tomography. It enjoys superior sensitivity to tenuous peripheral bronchioles, arterioles, and venules. The method first uses a feature recalibration module to make the best use of features learned from the neural networks. Spatial information of features is properly integrated to retain relative priority of activated regions, which benefits the subsequent channel-wise recalibration. Then, attention distillation module is introduced to reinforce representation learning of tubular objects. Fine-grained details in high-resolution attention maps are passing down from one layer to its previous layer recursively to enrich context. Anatomy prior of lung context map and distance transform map is designed and incorporated for better artery-vein differentiation capacity. Extensive experiments demonstrated considerable performance gains brought by these components. Compared with state-of-the-art methods, our method extracted much more branches while maintaining competitive overall segmentation performance. Codes and models will be available at this http://www.pami.sjtu.edu.cn

百度网盘（Baidu NetDisk）

下载链接（Download link）：https://pan.baidu.com/s/1KZPCs02qYobqjCdOXKeZcg

提取码（Download code）：ofcg

谷歌网盘（Google Drive）：

https://drive.google.com/file/d/16AQjDkEEeBbhJ7zJ9_0R6YjhK5jRLVIJ/view?usp=sharing