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

Sundaresh Ram,Aamer Chughtai,Timothy D Johnson,Aleksa B Fortuna,Alexander J Bell,Mark O Wielpütz,Meilan K Han,Beverley Newman,Dharshan Vummidi,Stefanie Galban,Ella A Kazerooni,Craig J Galban,Charles R Hatt,Oliver Weinheimer,Terry E Robinson,Benjamin A Hoff

doi:10.1371/journal.pone.0248902

Abstract

BackgroundRadiologic evidence of air trapping (AT) on expiratory computed tomography (CT) scans is associated with early pulmonary dysfunction in patients with cystic fibrosis (CF). However, standard techniques for quantitative assessment of AT are highly variable, resulting in limited efficacy for monitoring disease progression.ObjectiveTo investigate the effectiveness of a convolutional neural network (CNN) model for quantifying and monitoring AT, and to compare it with other quantitative AT measures obtained from threshold-based techniques.Materials and methodsPaired volumetric whole lung inspiratory and expiratory CT scans were obtained at four time points (0, 3, 12 and 24 months) on 36 subjects with mild CF lung disease. A densely connected CNN (DN) was trained using AT segmentation maps generated from a personalized threshold-based method (PTM). Quantitative AT (QAT) values, presented as the relative volume of AT over the lungs, from the DN approach were compared to QAT values from the PTM method. Radiographic assessment, spirometric measures, and clinical scores were correlated to the DN QAT values using a linear mixed effects model.ResultsQAT values from the DN were found to increase from 8.65% ± 1.38% to 21.38% ± 1.82%, respectively, over a two-year period. Comparison of CNN model results to intensity-based measures demonstrated a systematic drop in the Dice coefficient over time (decreased from 0.86 ± 0.03 to 0.45 ± 0.04). The trends observed in DN QAT values were consistent with clinical scores for AT, bronchiectasis, and mucus plugging. In addition, the DN approach was found to be less susceptible to variations in expiratory deflation levels than the threshold-based approach.ConclusionThe CNN model effectively delineated AT on expiratory CT scans, which provides an automated and objective approach for assessing and monitoring AT in CF patients.

Highlights

High resolution computed tomography (CT) is an integral tool for the treatment and management of patients with diffuse lung disease [1]
Applying a hard threshold of -856HU captured 3.8% of the total lung volume as air trapping (AT), which increased to 7.8% using the personalized threshold-based method (PTM) approach
We set out to demonstrate the utility of our densely connected Convolutional neural networks (CNNs) (DN) model to more accurately quantify the extent of AT on chest CT image acquisitions in a cohort of pediatric cystic fibrosis (CF) patients

Summary

Introduction

High resolution CT is an integral tool for the treatment and management of patients with diffuse lung disease [1]. The most extensively used method is quantification of low attenuation areas using a Hounsfield unit (HU) threshold-based approach, which defines areas at or below a static attenuation value as AT This approach was first applied to emphysema, and has been pathologically validated [4,5,6,7]. Simple to use and readily available, these attenuation threshold-based methods are known to be sensitive to scanner noise, reconstruction kernel, and the level of expiration at which the CT images were obtained [14]. This can create a discordance between the algorithm output and visual findings of AT by radiologists on the same chest CT images. Standard techniques for quantitative assessment of AT are highly variable, resulting in limited efficacy for monitoring disease progression

Methods

Results

Conclusion