The segmentation of pulmonary arteries and veins in computed tomography scans is crucial for the diagnosis and assessment of pulmonary diseases. This paper discusses the challenges in segmenting these vascular structures, such as the classification of terminal pulmonary vessels relying on information from distant root vessels, and the complex branches and crossings of arteriovenous vessels. To address these difficulties, we introduce a fully automatic segmentation method that utilizes multiple 3D residual U-blocks module, a semantic embedding module, and a semantic perception module. The 3D residual U-blocks module can extract multi-scale features under a high receptive field, the semantic embedding module embeds semantic information to aid the network in utilizing the anatomical characteristics of parallel pulmonary artery and bronchi, and the SPM perceives semantic information and decodes it into classification results for pulmonary arteries and veins. Our approach was evaluated on a dataset of 57 lung CT scans and demonstrated competitive performance compared to existing medical image segmentation models.
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