Abstract
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.
Highlights
Diagnostics of diffuse parenchymal lung disease (DPLD) is to this day a complex task performed using the collective information from clinical, radiological and histological criteria and analysed in a multidisciplinary discussion [1,2,3].even gold standard imaging methods such as high resolution computer tomography (HRCT) [4,5] struggle to yield consensus with regards to diagnosis in DPLD with a large interreader variability [5], advocating a need for additional diagnostic information in equivocal cases [6]
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
Summary
Even gold standard imaging methods such as high resolution computer tomography (HRCT) [4,5] struggle to yield consensus with regards to diagnosis in DPLD with a large interreader variability [5], advocating a need for additional diagnostic information in equivocal cases [6]. For this reason, pathological specimens are sometimes necessary in the diagnostic workup, such as surgical lung biopsy and more recently bronchoscopic cryobiopsy [7]. The aim of this study was to develop a fully automatic workflow for quantifying these structural properties using pCLE measurements and to perform a preliminary assessment of their diagnostic potential
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