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Mairhörmann, B. ; Castelblanco, A. ; Häfner, F. ; Koliogiannis, V. ; Haist, L. ; Winter, D. ; Flemmer, A.W. ; Ehrhardt, H. ; Stöcklein, S. ; Dietrich, O. ; Förster, K. ; Hilgendorff, A. ; Schubert, B.

Automated MRI lung segmentation and 3D morphologic features for quantification of neonatal lung disease.

Radiol. Artif. Intell. 5:e220239 (2023)

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PURPOSE: To analyze the performance of deep learning (DL) models for segmentation of the neonatal lung in MRI and investigate the use of automated MRI-based features for assessment of neonatal lung disease. MATERIALS AND METHODS: Quiet-breathing MRI was prospectively performed in two independent cohorts of preterm infants (median gestational age, 26.57 weeks; IQR, 25.3-28.6 weeks; 55 female and 48 male infants) with (n = 86) and without (n = 21) chronic lung disease (bronchopulmonary dysplasia [BPD]). Convolutional neural networks were developed for lung segmentation, and a three-dimensional reconstruction was used to calculate MRI features for lung volume, shape, pixel intensity, and surface. These features were explored as indicators of BPD and disease-associated lung structural remodeling through correlation with lung injury scores and multinomial models for BPD severity stratification. RESULTS: The lung segmentation model reached a volumetric Dice coefficient of 0.908 in cross-validation and 0.880 on the independent test dataset, matching expert-level performance across disease grades. MRI lung features demonstrated significant correlations with lung injury scores and added structural information for the separation of neonates with BPD (BPD vs no BPD: average area under the receiver operating characteristic curve [AUC], 0.92 ± 0.02 [SD]; no or mild BPD vs moderate or severe BPD: average AUC, 0.84 ± 0.03). CONCLUSION: This study demonstrated high performance of DL models for MRI neonatal lung segmentation and showed the potential of automated MRI features for diagnostic assessment of neonatal lung disease while avoiding radiation exposure.Keywords: Bronchopulmonary Dysplasia, Chronic Lung Disease, Preterm Infant, Lung Segmentation, Lung MRI, BPD Severity Assessment, Deep Learning, Lung Imaging Biomarkers, Lung Topology Supplemental material is available for this article. Published under a CC BY 4.0 license.See also the commentary by Parraga and Sharma in this issue.

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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Bpd Severity Assessment ; Bronchopulmonary Dysplasia ; Chronic Lung Disease ; Deep Learning ; Lung Imaging Biomarkers ; Lung Mri ; Lung Segmentation ; Lung Topology ; Preterm Infant; Bronchopulmonary Dysplasia; Perfusion

ISSN (print) / ISBN 2638-6100

e-ISSN 2638-6100

Journal Radiology. Artificial intelligence

Quellenangaben Volume: 5, Issue: 6, Article Number: e220239

Publisher Radiological Society of North America

Publishing Place 820 Jorie Blvd, Suite 200, Oak Brook, Illinois, United States

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of Computational Biology (ICB)
Lung Health and Immunity (LHI)

Grants BMBF
Postdoctoral Fellowship Program of the Helmholtz Zentrum Munchen
Helmholtz Association under the joint research school Munich School for Data Science-MUDS
Stiftung AtemWeg (LSS AIRR)
German Science and Research Organization (DFG)
German Center for Lung Research (Deutsches Zentrum fur Lungenforschung, German Ministry of Education and Health
Helmholtz Zentrum Munchen, Germany
Helmholtz Foundation