Abstract
Pulmonary artery-vein segmentation is crucial for diagnosing pulmonary diseases and surgical planning, and is traditionally achieved by Computed Tomography Pulmonary Angiography (CTPA). However, concerns regarding adverse health effects from contrast agents used in CTPA have constrained its clinical utility. In contrast, identifying arteries and veins using non-contrast CT, a conventional and low-cost clinical examination routine, has long been considered impossible. Here we propose a High-abundant Pulmonary Artery-vein Segmentation (HiPaS) framework achieving accurate artery-vein segmentation on both non-contrast CT and CTPA across various spatial resolutions. HiPaS first performs spatial normalization on raw CT scans via a super-resolution module, and then iteratively achieves segmentation results at different branch levels by utilizing the low-level vessel segmentation as a prior for high-level vessel segmentation. We trained and validated HiPaS on our established multi-centric dataset comprising 1,073 CT volumes with meticulous manual annotation. Both quantitative experiments and clinical evaluation demonstrated the superior performance of HiPaS, achieving a dice score of 91.8% and a sensitivity of 98.0%. Further experiments demonstrated the non-inferiority of HiPaS segmentation on non-contrast CT compared to segmentation on CTPA. Employing HiPaS, we have conducted an anatomical study of pulmonary vasculature on 10,613 participants in China (five sites), discovering a new association between pulmonary vessel abundance and sex and age: vessel abundance is significantly higher in females than in males, and slightly decreases with age, under the controlling of lung volumes (p < 0.0001). HiPaS realizing accurate artery-vein segmentation delineates a promising avenue for clinical diagnosis and understanding pulmonary physiology in a non-invasive manner.
Abstract (translated)
肺动脉-静脉分割对于诊断肺病和手术规划至关重要，传统上通过计算机断层扫描肺血管造影（CTPA）实现。然而，有关CTPA中使用的对比剂引起的 adverse health effects 使它的临床实用受到限制。相比之下，通过非对比增强CT（一种传统且成本较低的临床检查方法）来识别动脉和静脉多年来被认为是不可行的。 在这里，我们提出了一种高丰富度的肺动脉-静脉分割（HiPaS）框架，在非对比增强CT和CTPA的各种分辨率下实现精确的动脉-静脉分割。HiPaS首先通过超分辨率模块对原始CT扫描进行空间标准化，然后在不同分支级别上通过低级别血管分割作为高级别血管分割的预处理，实现分割结果。我们在中国对我们建立的多个中心数据集进行了训练和验证，该数据集包括1,073个CT volume。 HiPaS的定量实验和临床评估结果表明，HiPaS在实现准确动脉-静脉分割方面具有优越性能，达到91.8%的 dice score 和98.0%的敏感度。此外的实验结果表明，HiPaS在非对比增强CT上分割肺血管与在CTPA上分割肺血管的 non-inferiority。 利用HiPaS，我们在中国的10,613个参与者中进行了一次解剖研究（五个站点），发现了肺血管丰富性与性别和年龄之间的 new association：女性的肺血管丰富度显著高于男性，随着年龄的增长，肺血管丰富度略有下降，在控制肺容量的情况下（p < 0.0001）。HiPaS实现准确动脉-静脉分割揭示了通过非侵入性方法研究肺病和理解肺生理的有前景的途径。
URL
https://arxiv.org/abs/2404.07671
