Validation of automated lobe segmentation on paired inspiratory-expiratory chest CT in 8-14 year-old children with cystic fibrosis

Abstract

ObjectivesDensitometry on paired inspiratory and expiratory multidetector computed tomography (MDCT) for the quantification of air trapping is an important approach to assess functional changes in airways diseases such as cystic fibrosis (CF). For a regional analysis of functional deficits, an accurate lobe segmentation algorithm applicable to inspiratory and expiratory scans is beneficial.Materials and methodsWe developed a fully automated lobe segmentation algorithm, and subsequently validated automatically generated lobe masks (ALM) against manually corrected lobe masks (MLM). Paired inspiratory and expiratory CTs from 16 children with CF (mean age 11.1±2.4) acquired at 4 time-points (baseline, 3mon, 12mon, 24mon) with 2 kernels (B30f, B60f) were segmented, resulting in 256 ALM. After manual correction spatial overlap (Dice index) and mean differences in lung volume and air trapping were calculated for ALM vs. MLM.ResultsThe mean overlap calculated with Dice index between ALM and MLM was 0.98±0.02 on inspiratory, and 0.86±0.07 on expiratory CT. If 6 lobes were segmented (lingula treated as separate lobe), the mean overlap was 0.97±0.02 on inspiratory, and 0.83±0.08 on expiratory CT. The mean differences in lobar volumes calculated in accordance with the approach of Bland and Altman were generally low, ranging on inspiratory CT from 5.7±52.23cm3 for the right upper lobe to 17.41±14.92cm3 for the right lower lobe. Higher differences were noted on expiratory CT. The mean differences for air trapping were even lower, ranging from 0±0.01 for the right upper lobe to 0.03±0.03 for the left lower lobe.ConclusionsAutomatic lobe segmentation delivers excellent results for inspiratory and good results for expiratory CT. It may become an important component for lobe-based quantification of functional deficits in cystic fibrosis lung disease, reducing necessity for user-interaction in CT post-processing.

Mucus plugging, air trapping, and bronchiectasis are important outcome measures in assessing progressive childhood cystic fibrosis lung disease

“…The overall changes in most of our parameters over 2 years are comparable to or smaller than those previously reported for comparable longer-term epidemiologic studies in cystic fibrosis. These 2,19,20 and quantitative based airway analysis by Kuo et al 3,4 Our study has several limitations. First, conclusions may be limited by the relatively small complete sample size of 36 subjects.…”

“…Variability in scoring represents a meaningful limitation of reliance on reader‐based CT interpretation and emphasizes the importance of incorporating objective approaches for the interpretation of features, such as bronchial dilatation, air trapping, bronchial wall thickness, and mucus plugging. Promising existing or new quantitative based CT approaches include PRAGMA‐CF analysis, 1 quantitative airway and lobar air trapping assessment, 2,19,20 and quantitative based airway analysis by Kuo et al 3,4 …”

Improved detection of air trapping on expiratory computed tomography using deep learning

“…The prospective multi-center study was carried out in 36 subjects enrolled in the Novartis CF Natural History Study [ 25 ] from 2007–2011 and was approved by the Institutional Review Boards of Stanford University Medical Center (IRB #6218) and Ohio State University School of Medicine (IRB #07–00207). Informed written consent for examination and further data processing was obtained from all patients or legal guardians prior to inclusion.…”

Improved Detection of Air Trapping on Expiratory Computed Tomography Using Deep Learning

“…All subjects had a confirmed diagnosis of CF by pilocarpine iontophoresis sweat chloride testing and CF gene mutation analysis. The CF data has been used earlier to demonstrate the effects that CT registration has on quantifying air-trapping [25], and airway measurements [26] as well as quantifying lobar segmentation [27, 28] to show that clinical measures such as mucus plugging and bronchiectasis are important in assessing childhood CF. Here, we use this data and propose a CNN model to quantify AT.…”