计算机科学
分割
人工智能
对比度(视觉)
特征(语言学)
霍恩斯菲尔德秤
计算机视觉
图像分割
模式识别(心理学)
计算机断层摄影术
放射科
医学
哲学
语言学
作者
Weijian Zhou,Hui Zhang,Dongdong Gu,Sheng Wang,Jiayu Huo,Rui Zhang,Zhihao Jiang,Feng Shi,Xue Zhang,Yiqiang Zhan,Xin Ouyang,Dinggang Shen
标识
DOI:10.1007/978-3-031-43898-1_53
摘要
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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