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Abstract #4355

Deep Learning Lung Segmentation in Paediatric Patients

Orso Pusterla1,2, Simon Andermatt2, Grzegorz Bauman1,2, Sylvia Nyilas3, Philipp Madörin1, Tanja Haas1, Simon Pezold2, Francesco Santini1,2, Philipp Latzin3, Philippe Claude Cattin2, and Oliver Bieri1,2

1Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland, 2Department of Biomedical Engineering, University of Basel, Basel, Switzerland, 3Division of Respiratory Medicine, Department of Pediatrics, Children's Hospital of Bern, Bern, Switzerland

Automatic lung segmentation of MR images is challenging; especially in the presence of pathologies. In this work, we tackle lung segmentation of 2D and 3D ultra-fast steady-state free precession MRI in cystic fibrosis patients by using deep learning based on a neural network of multi-dimensional gated recurrent units.

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