Inter-reader variation in lung segmentation of functional lung MRI quantification

Abstract

Background: Functional lung imaging with MRI allows evaluation of ventilation and perfusion deficits in lung disease. To quantify deficits, segmentation of lung tissue is required, which is subjective and time-consuming. Deep learning (DL) algorithms could accelerate this process with high precision.

Aim: We assessed the variation in relative impaired ventilation and perfusion resulting from segmentations from different readers.

Methods: This study included 29 MRI scans from 17 children with cystic fibrosis and 9 healthy. Pulmonary tissue was segmented on base images. A matrix pencil algorithm computed perfusion- and ventilation-weighted maps of the lung and calculated the relative fractional ventilation (R_FV) and perfusion (R_Q) impairment. The R_FV and R_Q resulting from the segmentations of two experienced human readers (A & B) and a recurrent neural network (DL) were compared. Reader A repeated segmentation after 24 hours to investigate intra-reader variability. Agreement between readers was assessed with paired t-test and intra-class correlation coefficient (ICC).

Results: There was very good agreement between all readers (ICC: 0.98; 0.92 - 0.99). There was a significant difference in R_FV from reader A (mean=26.8; SD=6.4) to B (28.2; 6.1; p<0.001) and DL (28.0; 6.3; p<0.001). No significant differences in repeated segmentation from reader A (difference: -0.03; p=0.62) were found. The differences for R_Q were comparable to R_FV.

Conclusion: We found small but statistically significant differences in outcomes between observers. The inter-reader variability of R_FV and R_Q between human and machine was similar to the variability between human readers. DL may be a promising alternative to human segmentation.