{"ID":2890399,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.19074","arxiv_id":"2507.19074","title":"A Self-training Framework for Semi-supervised Pulmonary Vessel Segmentation and Its Application in COPD","abstract":"Background: It is fundamental for accurate segmentation and quantification of the pulmonary vessel, particularly smaller vessels, from computed tomography (CT) images in chronic obstructive pulmonary disease (COPD) patients. Objective: The aim of this study was to segment the pulmonary vasculature using a semi-supervised method. Methods: In this study, a self-training framework is proposed by leveraging a teacher-student model for the segmentation of pulmonary vessels. First, the high-quality annotations are acquired in the in-house data by an interactive way. Then, the model is trained in the semi-supervised way. A fully supervised model is trained on a small set of labeled CT images, yielding the teacher model. Following this, the teacher model is used to generate pseudo-labels for the unlabeled CT images, from which reliable ones are selected based on a certain strategy. The training of the student model involves these reliable pseudo-labels. This training process is iteratively repeated until an optimal performance is achieved. Results: Extensive experiments are performed on non-enhanced CT scans of 125 COPD patients. Quantitative and qualitative analyses demonstrate that the proposed method, Semi2, significantly improves the precision of vessel segmentation by 2.3%, achieving a precision of 90.3%. Further, quantitative analysis is conducted in the pulmonary vessel of COPD, providing insights into the differences in the pulmonary vessel across different severity of the disease. Conclusion: The proposed method can not only improve the performance of pulmonary vascular segmentation, but can also be applied in COPD analysis. The code will be made available at https://github.com/wuyanan513/semi-supervised-learning-for-vessel-segmentation.","short_abstract":"Background: It is fundamental for accurate segmentation and quantification of the pulmonary vessel, particularly smaller vessels, from computed tomography (CT) images in chronic obstructive pulmonary disease (COPD) patients. Objective: The aim of this study was to segment the pulmonary vasculature using a semi-supervis...","url_abs":"https://arxiv.org/abs/2507.19074","url_pdf":"https://arxiv.org/pdf/2507.19074v1","authors":"[\"Shuiqing Zhao\",\"Meihuan Wang\",\"Jiaxuan Xu\",\"Jie Feng\",\"Wei Qian\",\"Rongchang Chen\",\"Zhenyu Liang\",\"Shouliang Qi\",\"Yanan Wu\"]","published":"2025-07-25T08:50:31Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":611772,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2890399,"paper_url":"https://arxiv.org/abs/2507.19074","paper_title":"A Self-training Framework for Semi-supervised Pulmonary Vessel Segmentation and Its Application in COPD","repo_url":"https://github.com/wuyanan513/semi-supervised-learning-for-vessel-segmentation","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}