MRI lung lobe segmentation of pediatric cystic fibrosis patients using a neural 
network trained with publicly accessible CT datasets

Pusterla, O; Heule, R; Santini, F; Weikert, T; Willers, C; Andermatt, S; Sandkühler, R; Nyilas, S; Latzin, P; Bieri, O; Bauman, G

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MRI lung lobe segmentation of pediatric cystic fibrosis patients using a neural network trained with publicly accessible CT datasets

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Heule, R
Institutional Guests, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://submissions.mirasmart.com/ISMRM2022/itinerary/ConferenceMatrixEventDetail.aspx?id=384
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Citation

Pusterla, O., Heule, R., Santini, F., Weikert, T., Willers, C., Andermatt, S., et al. (2022). MRI lung lobe segmentation of pediatric cystic fibrosis patients using a neural network trained with publicly accessible CT datasets. Poster presented at Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting (ISMRM 2022), London, UK.

Cite as: https://hdl.handle.net/21.11116/0000-000A-5CE4-E

Abstract

Pulmonary biomarkers quantifications on a lobar level provide improved specificity against whole-lung analyses. However, lobar quantifications of pulmonary MR data are hardly accessible due to the complex work required for the manual segmentations. Supervised neural networks have shown the premise for automatic segmentation, but it is challenging to gather labelled data for the training. To overcome these limitations, in this work, we “translate” publicly accessible chest CT datasets and lobe segmentations to pseudo-MR data, and we then train a network able to segment consistently lung lobes of acquired MRI data. The cross-modality approach has excellent prospects to automatize MRI analyses.