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

Willers, Corin; Bauman, Grzegorz; Andermatt, Simon; Santini, Francesco; Sandkühler, Robin; Ramsey, Kathryn A.; Cattin, Philippe C; Bieri, Oliver; Pusterla, Orso; Latzin, Philipp (2021). The impact of segmentation on whole-lung functional MRI quantification: Repeatability and reproducibility from multiple human observers and an artificial neural network. Magnetic resonance in medicine, 85(2), pp. 1079-1092. Wiley-Liss 10.1002/mrm.28476

[img] Text
The impact of segmentation on whole-lung functional MRI quantification_Repeatability and reproducibility from multiple human observers and an artificial neural network.pdf - Published Version
Restricted to registered users only
Available under License Publisher holds Copyright.

Download (2MB) | Request a copy
[img] Text
Impact_of_segmentation_on_functional_lung_MRI_WillersC_20200713_MRMbiblio_revised_clean_with_Fig.pdf - Accepted Version
Restricted to registered users only until 7 September 2021.
Available under License Publisher holds Copyright.

Download (1MB) | Request a copy

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 (RFV ) and relative perfusion (RQ ) from MP-MRI were compared using whole-lung segmentation performed by a physician at two time-points (At1 and At2 ), 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 (At1 vs At2 ) resulted in a DSC of 0.94 ± 0.01 (mean ± SD) and an unsystematic difference of -0.01% for RFV (P = .92) and +0.1% for RQ (P = .21). The reproducibility between human observers (At1 vs B) resulted in a DSC of 0.88 ± 0.02, and a systematic absolute difference of -0.81% (P < .001) for RFV and -0.38% (P = .037) for RQ . The reproducibility between human and the ANN (At1 vs C) resulted in a DSC of 0.89 ± 0.03 and a systematic absolute difference of -0.36% for RFV (P = .017) and -0.35% for RQ (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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Paediatric Medicine
04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Paediatric Medicine > Paediatric Pneumology

UniBE Contributor:

Willers, Christoph Corin; Ramsey, Kathryn Angela and Latzin, Philipp

Subjects:

600 Technology > 610 Medicine & health

ISSN:

0740-3194

Publisher:

Wiley-Liss

Language:

English

Submitter:

Romy Melanie Rodriguez Del Rio

Date Deposited:

02 Dec 2020 10:16

Last Modified:

11 Dec 2020 07:48

Publisher DOI:

10.1002/mrm.28476

PubMed ID:

32892445

Uncontrolled Keywords:

automated segmentation functional lung MRI inter-reader reproducibility neural networks pediatrics

BORIS DOI:

10.7892/boris.147756

URI:

https://boris.unibe.ch/id/eprint/147756

Actions (login required)

Edit item Edit item
Provide Feedback