Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
Comprehensive Pneumology Center (CPC-M), University Hospital, LMU Munich, Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany.
PLoS One. 2020 May 6;15(5):e0232847. doi: 10.1371/journal.pone.0232847. eCollection 2020.
Probe-based confocal endomicroscopy provides real time videos of autoflourescent elastin structures within the alveoli. With it, multiple changes in the elastin structure due to different diffuse parenchymal lung diseases have previously been described. However, these evaluations have mainly relied on qualitative evaluation by the examiner and manually selected parts post-examination.
To develop a fully automatic method for quantifying structural properties of the imaged alveoli elastin and to perform a preliminary assessment of their diagnostic potential.
46 patients underwent probe-based confocal endomicroscopy, of which 38 were divided into 4 groups categorizing different diffuse parenchymal lung diseases. 8 patients were imaged in representative healthy lung areas and used as control group. Alveolar elastin structures were automatically segmented with a trained machine learning algorithm and subsequently evaluated with two methods developed for quantifying the local thickness and structural connectivity.
The automatic segmentation algorithm performed generally well and all 4 patient groups showed statistically significant differences with median elastin thickness, standard deviation of thickness and connectivity compared to the control group.
Alveoli elastin structures can be quantified based on their structural connectivity and thickness statistics with a fully-automated algorithm and initial results highlight its potential for distinguishing parenchymal lung diseases from normal alveoli.
基于探针的共聚焦内窥镜提供了肺泡内自发荧光弹性结构的实时视频。利用它，先前已经描述了由于不同弥漫性实质性肺疾病导致的弹性结构的多种变化。然而，这些评估主要依赖于检查者的定性评估以及检查后的手动选择部分。
开发一种定量分析成像肺泡弹性蛋白结构特性的全自动方法，并对其诊断潜力进行初步评估。
46 名患者接受了基于探针的共聚焦内窥镜检查，其中 38 名患者分为 4 组，分类为不同的弥漫性实质性肺疾病。8 名患者在代表性的健康肺区进行成像，作为对照组。使用经过训练的机器学习算法对肺泡弹性结构进行自动分割，然后使用两种方法对局部厚度和结构连通性进行评估。
自动分割算法总体表现良好，与对照组相比，所有 4 组患者的弹性蛋白厚度中位数、厚度标准差和连通性均有统计学显著差异。
可以使用全自动算法基于结构连通性和厚度统计数据对肺泡弹性结构进行定量分析，初步结果突出了其区分实质肺疾病与正常肺泡的潜力。
