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Towards Automated Multi-regional Lung Parcellation for 0.55-3T 3D T2w Fetal MRI

Uus AU, Zampieri CA, Downes F, Collado AE, Hall M, Davidson J, Payette K, Verdera JA, Grigorescu I, Hajnal JV, Deprez M, Aertsen M, Hutter J, Rutherford MA, Deprest J, Story L (2025)

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Publication Type: Conference contribution

Publication year: 2025

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 14747 LNCS

Pages Range: 119-129

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Marrakesh, MAR

ISBN: 9783031732591

DOI: 10.1007/978-3-031-73260-7_11

Abstract

Fetal MRI is increasingly being employed in the diagnosis of fetal lung anomalies and segmentation-derived total fetal lung volumes are used as one of the parameters for prediction of neonatal outcomes. However, in clinical practice, segmentation is performed manually in 2D motion-corrupted stacks with thick slices which is time consuming and can lead to variations in estimated volumes. Furthermore, there is a known lack of consensus regarding a universal lung parcellation protocol and expected normal total lung volume formulas. The lungs are also segmented as one label without parcellation into lobes. In terms of automation, to the best of our knowledge, there have been no reported works on multi-lobe segmentation for fetal lung MRI. This work introduces the first automated deep learning segmentation pipeline for multi-regional lung segmentation for 3D motion-corrected T2w fetal body images for normal anatomy and congenital diaphragmatic hernia cases. The protocol for parcellation into 5 standard lobes was defined in the population-averaged 3D atlas. It was then used to generate a multi-label training dataset including 104 normal anatomy controls and 45 congenital diaphragmatic hernia cases from 0.55T, 1.5T and 3T acquisition protocols. The performance of 3D Attention UNet network was evaluated on 18 cases and showed good results for normal lung anatomy with expectedly lower Dice values for the ipsilateral lung. In addition, we also produced normal lung volumetry growth charts from 290 0.55T and 3T controls. This is the first step towards automated multi-regional fetal lung analysis for 3D fetal MRI.

Involved external institutions

How to cite

APA:

Uus, A.U., Zampieri, C.A., Downes, F., Collado, A.E., Hall, M., Davidson, J.,... Story, L. (2025). Towards Automated Multi-regional Lung Parcellation for 0.55-3T 3D T2w Fetal MRI. In Daphna Link-Sourani, Esra Abaci Turk, Christopher Macgowan, Jana Hutter, Andrew Melbourne, Jana Hutter, Roxane Licandro (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 119-129). Marrakesh, MAR: Springer Science and Business Media Deutschland GmbH.

MLA:

Uus, Alena U., et al. "Towards Automated Multi-regional Lung Parcellation for 0.55-3T 3D T2w Fetal MRI." Proceedings of the 9th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2024, held in Conjunction with the 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024, Marrakesh, MAR Ed. Daphna Link-Sourani, Esra Abaci Turk, Christopher Macgowan, Jana Hutter, Andrew Melbourne, Jana Hutter, Roxane Licandro, Springer Science and Business Media Deutschland GmbH, 2025. 119-129.

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