Abstract #3379

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

Orso Pusterla^1,2,3, Rahel Heule^4,5, Francesco Santini^1,3,6, Thomas Weikert⁶, Corin Willers², Simon Andermatt³, Robin Sandkühler³, Sylvia Nyilas⁷, Philipp Latzin², Oliver Bieri^1,3, and Grzegorz Bauman^1,3

¹Division of Radiological Physics, Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland, ²Division of Pediatric Respiratory Medicine and Allergology, Department of Pediatrics, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland, ³Department of Biomedical Engineering, University of Basel, Basel, Switzerland, ⁴High Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübinge, Germany, ⁵Department of Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany, ⁶Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland, ⁷Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland

Synopsis

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.

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