Pulmonary emphysema is a destructive disease of the lungs that is currently diagnosed via visual assessment of lung Computed Tomography (CT) scans by a radiologist. Visual assessment can have poor inter-rater reliability, is time consuming, and requires access to trained assessors. Quantitative methods that reliably summarize the biologically relevant characteristics of an image are needed to improve the way lung diseases are characterized. The goal of this work was to show how spatial point process models can be used to create a set of radiologically derived quantitative lung biomarkers of emphysema. We formalized a general framework for applying spatial point processes to lung CT scans, and developed a Shot Noise Cox Process to quantify how radiologically based emphysematous tissue clusters into larger structures. Bayesian estimation of model parameters was done using spatial Birth–Death MCMC (BD-MCMC). In simulations, we showed the BD-MCMC estimation algorithm is able to accurately recover model parameters. In an application to real lung CT scans from the COPDGene cohort, we showed variability in the clustering characteristics of emphysematous tissue across disease subtypes that were based on visual assessments of the CT scans.

肺气肿是一种破坏性的肺部疾病，目前由放射科医生通过对肺部计算机断层扫描 (CT) 扫描进行视觉评估来诊断。视觉评估的评估者间可靠性较差、耗时且需要训练有素的评估员。需要可靠地总结图像的生物学相关特征的定量方法来改进肺部疾病的表征方式。这项工作的目标是展示如何使用空间点过程模型来创建一组放射学衍生的定量肺气肿肺生物标志物。我们正式制定了将空间点过程应用于肺部 CT 扫描的通用框架，并开发了散粒噪声 Cox 过程来量化基于放射学的肺气肿组织如何聚集成更大的结构。使用空间出生-死亡 MCMC (BD-MCMC) 进行模型参数的贝叶斯估计。在仿真中，我们表明 BD-MCMC 估计算法能够准确地恢复模型参数。在 COPDGene 队列的真实肺部 CT 扫描应用中，我们基于 CT 扫描的视觉评估，显示了不同疾病亚型的肺气肿组织的聚类特征的差异。