Automated evaluation of probe-based confocal laser endomicroscopy in the lung
Automated evaluation of probe-based confocal laser endomicroscopy in the lung
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DOI：
10.1371/journal.pone.0232847
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发表时间：
2020-05-06
期刊：
影响因子：
3.7
通讯作者：
Dinkel, Julien
中科院分区：
文献类型：
--
作者：
Bondesson, David;Schneider, Moritz J.;Dinkel, Julien
基于荧光探针的共聚焦显微内镜提供了肺泡内自体荧光弹性蛋白结构的真实的时间视频。有了它，弹性蛋白结构的多种变化，由于不同的弥漫性肺实质疾病先前已被描述。然而，这些评价主要依赖于检查者的定性评价和检查后手动选择的部分。ObjectivesTo开发一种全自动的方法来量化成像肺泡弹性蛋白的结构特性，并对其诊断潜力进行初步评估。其中38例根据弥漫性肺实质病变分为4组。8名患者在代表性健康肺区域中成像并用作对照组。用训练过的机器学习算法自动分割肺泡弹性蛋白结构，随后用两种方法进行评估，这两种方法是为了量化局部厚度和结构连接性而开发的。测量和主要结果自动分割算法总体上表现良好，所有4个患者组的中值弹性蛋白厚度显示出统计学显著差异，与对照组相比，厚度和连接性的标准差。结论肺泡弹性蛋白结构可以根据其结构连接性和厚度统计数据进行量化，自动算法和初步结果突出了其区分实质性肺疾病与正常肺泡的潜力。
RationaleProbe-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.ObjectivesTo develop a fully automatic method for quantifying structural properties of the imaged alveoli elastin and to perform a preliminary assessment of their diagnostic potential.Methods46 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.Measurements and main resultsThe 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.ConclusionAlveoli 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.