A fully automated pipeline for the extraction of pectoralis muscle area from chest computed tomography scans.

Background: Computed tomography (CT)-derived pectoralis muscle area (PMA) measurements are prognostic in people with or at-risk of COPD, but fully automated PMA extraction has yet to be developed. Our objective was to develop and validate a PMA extraction pipeline that can automatically: 1) identify the aortic arch slice; and 2) perform pectoralis segmentation at that slice. Methods: CT images from the Canadian Cohort of Obstructive Lung Disease (CanCOLD) study were used for pipeline development. Aorta atlases were used to automatically identify the slice containing the aortic arch by group-based registration. A deep learning model was trained to segment the PMA. The pipeline was evaluated in comparison to manual segmentation. An external dataset was used to evaluate generalisability. Model performance was assessed using the Dice-Sorensen coefficient (DSC) and PMA error. Results: In total 90 participants were used for training (age 67.0±9.9 years; forced expiratory volume in 1 s (FEV1) 93±21% predicted; FEV1/forced vital capacity (FVC) 0.69±0.10; 47 men), and 32 for external testing (age 68.6±7.4 years; FEV1 65±17% predicted; FEV1/FVC 0.50±0.09; 16 men). Compared with manual segmentation, the deep learning model achieved a DSC of 0.94±0.02, 0.94±0.01 and 0.90±0.04 on the true aortic arch slice in the train, validation and external test sets, respectively. Automated aortic arch slice detection obtained distance errors of 1.2±1.3 mm and 1.6±1.5 mm on the train and test data, respectively. Fully automated PMA measurements were not different from manual segmentation (p>0.05). PMA measurements were different between people with and without COPD (p=0.01) and correlated with FEV1 % predicted (p<0.05). Conclusion: A fully automated CT PMA extraction pipeline was developed and validated for use in research and clinical practice.

Semiautomated Segmentation and Analysis of Airway Lumen in Pediatric Patients Using Ultra Short Echo Time MRI.

RATIONALE AND OBJECTIVES: Ultra short echo time (UTE) magnetic resonance imaging (MRI) pulse sequences have shown promise for airway assessment, but the feasibility and repeatability in the pediatric lung are unknown. The purpose of this work was to develop a semiautomated UTE MRI airway segmentation pipeline from the trachea-to-tertiary airways in pediatric participants and assess repeatability and lumen diameter correlations to lung function. MATERIALS AND METHODS: A total of 29 participants (n = 7 healthy, n = 11 cystic fibrosis, n = 6 asthma, and n = 5 ex-preterm), aged 7-18 years, were imaged using a 3D stack-of-spirals UTE examination at 3 T. Two independent observers performed airway segmentations using a pipeline developed in-house; observer 1 repeated segmentations 1 month later. Segmentations were extracted using region-growing with leak detection, then manually edited if required. The airway trees were skeletonized, pruned, and labeled. Airway lumen diameter measurements were extracted using ray casting. Intra- and interobserver variability was assessed using the Sørensen-Dice coefficient (DSC) and intra-class correlation coefficient (ICC). Correlations between lumen diameter and pulmonary function were assessed using Spearman's correlation coefficient. RESULTS: For airway segmentations and lumen diameter, intra- and interobserver DSCs were 0.88 and 0.80, while ICCs were 0.95 and 0.89, respectively. The variability increased from the trachea-to-tertiary airways for intra- (DSC: 0.91-0.64; ICC: 0.91-0.49) and interobserver (DSC: 0.84-0.51; ICC: 0.89-0.21) measurements. Lumen diameter was significantly correlated with forced expiratory volume in 1 second and forced vital capacity (P < .05). CONCLUSION: UTE MRI airway segmentation from the trachea-to-tertiary airways in pediatric participants across a range of diseases is feasible. The UTE MRI-derived lumen measurements were repeatable and correlated with lung function.