RT Journal Article
SR Electronic
T1 Inter-reader variation in lung segmentation of functional lung MRI quantification
JF European Respiratory Journal
JO Eur Respir J
FD European Respiratory Society
SP PA333
DO 10.1183/13993003.congress-2019.PA333
VO 54
IS suppl 63
A1 Willers, Christoph Corin
A1 Pusterla, Orso
A1 Bauman, Grzegorz
A1 Andermatt, Simon
A1 Nyilas, Sylvia
A1 Santini, Francesco
A1 Pezold, Simon
A1 Sandkühler, Robin
A1 Ramsey, Kathryn
A1 Cattin, Philippe C.
A1 Bieri, Oliver
A1 Latzin, Philipp
YR 2019
UL http://erj.ersjournals.com/content/54/suppl_63/PA333.abstract
AB 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 (RFV) and perfusion (RQ) impairment. The RFV and RQ 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 RFV 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 RQ were comparable to RFV.Conclusion: We found small but statistically significant differences in outcomes between observers. The inter-reader variability of RFV and RQ between human and machine was similar to the variability between human readers. DL may be a promising alternative to human segmentation.FootnotesCite this article as: European Respiratory Journal 2019; 54: Suppl. 63, PA333.This is an ERS International Congress abstract. No full-text version is available. Further material to accompany this abstract may be available at www.ers-education.org (ERS member access only).