Abstract
Pulmonary vessel segmentation in computerized tomography (CT) images is essential for pulmonary vascular disease and surgical navigation. However, the existing methods were generally designed for contrast-enhanced images, their performance is limited by the low contrast and the non-uniformity of Hounsfield Unit (HU) in non-contrast CT images, meanwhile, the varying size of the vessel structures are not well considered in current pulmonary vessel segmentation methods. To address this issue, we propose a hierarchical enhancement network (HENet) for better image- and feature-level vascular representation learning in the pulmonary vessel segmentation task. Specifically, we first design an Auto Contrast Enhancement (ACE) module to adjust the vessel contrast dynamically. Then, we propose a Cross-Scale Non-local Block (CSNB) to effectively fuse multi-scale features by utilizing both local and global semantic information. Experimental results show that our approach achieves better pulmonary vessel segmentation outcomes compared to other state-of-the-art methods, demonstrating the efficacy of the proposed ACE and CSNB module. Our code is available at https://github.com/CODESofWenqi/HENet.
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Acknowledgments
This work was supported in part by National Key Research and Development Program of China (2021ZD0111100), National Natural Science Foundation of China (62131015), and Science and Technology Commission of Shanghai Municipality (STCSM) (21010502600).
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Zhou, W. et al. (2023). HENet: Hierarchical Enhancement Network for Pulmonary Vessel Segmentation in Non-contrast CT Images. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14222. Springer, Cham. https://doi.org/10.1007/978-3-031-43898-1_53
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