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Abstract #2223

Lung segmentation with deep learning for 3D MR spirometry

Zhongzheng He1,2, Nathalie Barrau1, Claire Pellot Barakat1, and Xavier Maître1
1Université Paris-Saclay, CEA, CNRS, Inserm, BioMaps, Orsay, France, 2IADI U1254, INSERM, Université de Lorraine, Nancy, France


Current MR lung image segmentation has huge challenges compared to CT images, specifically in terms of low contrast, non-homogeneity. We developed a series of processing to accelerate the manual correction of reference standard lung masks by the auto-seeds region growing. Finally, we developed a 3D automatic MRI lung segmentation method using deep learning with a limited dataset (41 volumes for training and 4 volumes for validation). In the primary result, this lung segmentation archived a Dice score of 0.917±0.013. In the case of limited data, it provides us a new way for MR lung segmentation.

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