Improving pulmonary lobe segmentation on expiratory CTs by using aligned inspiratory CTs

Weinheimer, Oliver; Wielpütz, Mark O.; Konietzke, Philip; Kauczor, Hans‐Ulrich; Robinson, Terry E.; Galbán, Craig J.

doi:10.1117/12.2513174

Cited by 3 publications

(1 citation statement)

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“…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.…”

Section: Methodsmentioning

confidence: 99%

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

Ram

Hoff

Bell

et al. 2020

Preprint

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BackgroundRadiologic evidence of air trapping (AT) on expiratory 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 air trapping on expiratory CT scans, which provides an automated and objective approach for assessing and monitoring air trapping in CF patients.

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Section: Methodsmentioning

confidence: 99%

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

Ram

Hoff

Bell

et al. 2020

Preprint

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Quantitative CT detects progression in COPD patients with severe emphysema in a 3-month interval

et al. 2020

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

et al. 2021

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Background Radiologic 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. Objective To 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 methods Paired 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. Results QAT 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. Conclusion The CNN model effectively delineated AT on expiratory CT scans, which provides an automated and objective approach for assessing and monitoring AT in CF patients.

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Improving pulmonary lobe segmentation on expiratory CTs by using aligned inspiratory CTs

Cited by 3 publications

References 8 publications

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

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

Quantitative CT detects progression in COPD patients with severe emphysema in a 3-month interval

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

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