AI 中文总结
本研究以低剂量CT的支气管血管束分割为案例，构建RONALD无监督分割流程，在真实标注稀疏时，其结节保留率显著优于现有方法，可提升极早期肺癌的肺结节检测效果。
AI 中文摘要
背景：肺癌仍是全球致死率最高的癌症，因其常被诊断过晚，有效治疗依赖早期筛查阶段的检测。然而患者数量不断增长而放射科医师数量有限，导致诊断等待时间延长。在极早期肺癌中，结节能见度会因邻近血管和气道壁进一步降低，因为结节常与这些结构相连或由其供血，因此针对支气管血管束的特定分析对结节高效检测十分重要，去除该结构可提升肺癌筛查的诊断潜力。材料与方法：为评估所提方法的效能，使用了广泛应用的低剂量CT（LDCT）数据集系列，包括杜克肺癌筛查（DLCS）数据集和波美拉尼亚肺癌筛查计划试点数据集。所提支气管血管束分割流程RONALD，可对计算机断层扫描图像进行处理，返回肺实质内血管与支气管的二值掩码；该方法包含预处理阶段，需完成肺、肺叶及纵隔分割，随后分别进行血管与支气管树分割。结果：所提流程可对低剂量CT扫描中的支气管血管束进行分割，且相比其他分割方法提升了结节保留率：在DLCS数据集中，结节保留率从93.98%和90.36%提升至100%；在波美拉尼亚数据集中，从83.16%和62.36%提升至99.92%。结论：所得分割结果可提升极早期肺癌的肺结节检测效果。
英文摘要
Background Lung cancer remains the deadliest cancer worldwide because it is often diagnosed too late. Effective treatment depends on detection at an early screening stage. However, the growing number of patients and the limited number of radiologists lead to prolonged diagnostic waiting times. In very early stage lung cancer, nodule visibility is further reduced by adjacent blood vessels and airway walls, because nodules are often connected to or supplied by these structures. Task-specific analysis of the bronchovascular bundle is therefore important for efficient nodule detection, and its removal can increase the diagnostic potential of lung cancer screening. Materials and Methods To assess the efficacy of the proposed method, we used series from widely utilized LDCT datasets, including the Duke Lung Cancer Screening (DLCS) dataset and the Pilot Pomeranian Lung Cancer Screening Program. The proposed bronchovascular bundle segmentation pipeline, RONALD, operates on computed tomography images and returns binary masks of vessels and bronchi located in the lung parenchyma. The method includes a preprocessing stage with lung, lobe, and mediastinum segmentation, followed by separate vessel and bronchial tree segmentation. Results The proposed pipeline segmented the bronchovascular bundle in low-dose computed tomography scans while improving nodule retention compared with other segmentation methods: from 93.98% and 90.36% to 100% in DLCS, and from 83.16% and 62.36% to 99.92% in the Pomeranian dataset. Conclusion The resulting segmentations can improve lung nodule detection in the very early stages of lung cancer.
Comments17 pages, 4 figures. Part of this research was submitted to the international conference European Molecular Imaging Meeting 2026
