The impact of segmentation on whole-lung functional MRI quantification: Repeatability and reproducibility from multiple human observers and an artificial neural network.

Purpose: To investigate the repeatability and reproducibility of lung segmentation and their impact on the quantitative outcomes from functional pulmonary MRI. Additionally, to validate an artificial neural network (ANN) to accelerate whole-lung quantification.

Method: Ten healthy children and 25 children with cystic fibrosis underwent matrix pencil decomposition MRI (MP-MRI). Impaired relative fractional ventilation (R ) and relative perfusion (R ) from MP-MRI were compared using whole-lung segmentation performed by a physician at two time-points (A and A ), by an MRI technician (B), and by an ANN (C). Repeatability and reproducibility were assess with Dice similarity coefficient (DSC), paired t-test and Intraclass-correlation coefficient (ICC).

Results: The repeatability within an observer (A vs A ) resulted in a DSC of 0.94 ± 0.01 (mean ± SD) and an unsystematic difference of -0.01% for R (P = .92) and +0.1% for R (P = .21). The reproducibility between human observers (A vs B) resulted in a DSC of 0.88 ± 0.02, and a systematic absolute difference of -0.81% (P < .001) for R and -0.38% (P = .037) for R . The reproducibility between human and the ANN (A vs C) resulted in a DSC of 0.89 ± 0.03 and a systematic absolute difference of -0.36% for R (P = .017) and -0.35% for R (P = .002). The ICC was >0.98 for all variables and comparisons.

Conclusions: Despite high overall agreement, there were systematic differences in lung segmentation between observers. This needs to be considered for longitudinal studies and could be overcome by using an ANN, which performs as good as human observers and fully automatizes MP-MRI post-processing.