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<p>Pulmonary vascular diseases may affect arteries and veins through different physiological mechanisms, necessitating separate assessment of the two vascular trees. However, manual analysis of chest computed tomography (CT) images is time-consuming, subjective, and challenging to scale for clinical studies. To address this, we propose a novel Human-in-the-Loop (HITL) framework for annotation and model training to develop an automated pulmonary artery-vein segmentation model. Through three iterative HITL rounds, we constructed 30 gold-standard annotated datasets and trained three deep learning models. The optimal model achieved strong performance, with a Dice coefficient of 86.2%, IoU of 75.7%, sensitivity of 85.8%, and precision of 88.1%. This model was then applied to segment pulmonary arteries and veins in CT scans from patients with chronic obstructive pulmonary disease (COPD). Quantitative analysis revealed that, as COPD progresses, the volume and surface area of both pulmonary arteries and veins increase. Moreover, small vessel truncation becomes more pronounced, with small arteries showing greater structural loss than veins. These findings suggest distinct vascular remodeling patterns between arterial and venous trees across different disease stages. Our HITL-based framework not only enhances annotation efficiency but also supports the development of robust segmentation models. This approach holds promise for broader applications in medical image analysis and provides a valuable tool for characterizing vascular changes in COPD, contributing to improved diagnosis and patient management.</p> Graphical Abstract <p></p>

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Accurate segmentation of pulmonary arteries and veins via a human-in-the-loop framework with application in COPD

  • 期刊论文

  • 发表日期：

    2026年3月23日

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  • Patrice Monkam,
  • Xin Du,
  • Yanan Wu,
  • Tiande Zhang,
  • Jiaxuan Xu,
  • Jie Feng,
  • Rongchang Chen,
  • Zhenyu Liang,
  • Shouliang Qi

摘要

Pulmonary vascular diseases may affect arteries and veins through different physiological mechanisms, necessitating separate assessment of the two vascular trees. However, manual analysis of chest computed tomography (CT) images is time-consuming, subjective, and challenging to scale for clinical studies. To address this, we propose a novel Human-in-the-Loop (HITL) framework for annotation and model training to develop an automated pulmonary artery-vein segmentation model. Through three iterative HITL rounds, we constructed 30 gold-standard annotated datasets and trained three deep learning models. The optimal model achieved strong performance, with a Dice coefficient of 86.2%, IoU of 75.7%, sensitivity of 85.8%, and precision of 88.1%. This model was then applied to segment pulmonary arteries and veins in CT scans from patients with chronic obstructive pulmonary disease (COPD). Quantitative analysis revealed that, as COPD progresses, the volume and surface area of both pulmonary arteries and veins increase. Moreover, small vessel truncation becomes more pronounced, with small arteries showing greater structural loss than veins. These findings suggest distinct vascular remodeling patterns between arterial and venous trees across different disease stages. Our HITL-based framework not only enhances annotation efficiency but also supports the development of robust segmentation models. This approach holds promise for broader applications in medical image analysis and provides a valuable tool for characterizing vascular changes in COPD, contributing to improved diagnosis and patient management.

Graphical Abstract