TY - JOUR T1 - Inter-reader variation in lung segmentation of functional lung MRI quantification JF - European Respiratory Journal JO - Eur Respir J DO - 10.1183/13993003.congress-2019.PA333 VL - 54 IS - suppl 63 SP - PA333 AU - Christoph Corin Willers AU - Orso Pusterla AU - Grzegorz Bauman AU - Simon Andermatt AU - Sylvia Nyilas AU - Francesco Santini AU - Simon Pezold AU - Robin Sandkühler AU - Kathryn Ramsey AU - Philippe C. Cattin AU - Oliver Bieri AU - Philipp Latzin Y1 - 2019/09/28 UR - http://erj.ersjournals.com/content/54/suppl_63/PA333.abstract N2 - 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). ER -