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

• URL: http://arxiv.org/abs/2012.05767v5
• Date: Thu, 25 Feb 2021 08:22:26 GMT
• Title: Learning Tubule-Sensitive CNNs for Pulmonary Airway and Artery-Vein Segmentation in CT
• Authors: Yulei Qin, Hao Zheng, Yun Gu, Xiaolin Huang, Jie Yang, Lihui Wang, Feng Yao, Yue-Min Zhu, Guang-Zhong Yang
• Abstract summary: Training convolutional neural networks (CNNs) for segmentation of pulmonary airway, artery, and vein is challenging. 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.
• Score: 45.93021999366973
• License: http://creativecommons.org/licenses/by/4.0/
• 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 are available at http://www.pami.sjtu.edu.cn/News/56

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