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. 2020 May 6;15(5):e0232847.
doi: 10.1371/journal.pone.0232847. eCollection 2020.

Automated evaluation of probe-based confocal laser endomicroscopy in the lung

Affiliations

Automated evaluation of probe-based confocal laser endomicroscopy in the lung

David Bondesson et al. PLoS One. .

Abstract

Rationale: 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.

Objectives: 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.

Methods: 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.

Measurements and main results: 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.

Conclusion: 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.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Workflow for structural evaluation of alveoli elastin.
A) Example of acquired NSIP patient snapshot from pCLE screening. B) Resulting segmentation generated by pixel classification based on a machine learning approach. C) Generated skeleton of structure used when calculating local thickness. D) Visualization of local thickness for whole structure.
Fig 2
Fig 2. Three examples from every step of evaluation workflow.
A-D) represent normal alveoli elastin structure with Tmed = 17.5 μm, σT = 8.5 μm and Cs = 0.4 mm−1, E-H) represents IPF elastin structure with Tmed = 37.8 μm, σT = 21.9 μm and Cs = 5.1 mm−1. I-L) represents NSIP structure with Tmed = 24.7 μm, σT = 11.7 and Cs = 4.9 mm−1.
Fig 3
Fig 3. Value distributions of DPLD and normal structure evaluations for structural connectivity and thickness statistics.
Box-and-whisker plots displaying value ranges for Tmed, σT and Cs. Red line specifies group median. Bottom and top edges indicate the 25th and 75th percentiles, respectively and whiskers covers ± 2.698σ. *: p-value<0.05, **: p-value<0.01.

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