Automatic CT pulmonary artery-vein segmentation using 3D RSU and semantic embedding

Hao Qi; Ming Wu; Ao Wang; Zhiyuan Liu; Yinran Chen; Sunkui Ke; Chengwei Zhou; Xiongbiao Luo

doi:10.1117/12.3006365

2 April 2024 Automatic CT pulmonary artery-vein segmentation using 3D RSU and semantic embedding

Hao Qi, Ming Wu, Ao Wang, Zhiyuan Liu, Yinran Chen, Sunkui Ke, Chengwei Zhou, Xiongbiao Luo

Proceedings Volume 12930, Medical Imaging 2024: Clinical and Biomedical Imaging; 129302M (2024) https://doi.org/10.1117/12.3006365
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hao Qi, Ming Wu, Ao Wang, Zhiyuan Liu, Yinran Chen, Sunkui Ke, Chengwei Zhou, and Xiongbiao Luo "Automatic CT pulmonary artery-vein segmentation using 3D RSU and semantic embedding", Proc. SPIE 12930, Medical Imaging 2024: Clinical and Biomedical Imaging, 129302M (2 April 2024); https://doi.org/10.1117/12.3006365

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