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Abstract #1036

End-to-End Full Automated Pipeline using a Convolutional Neural Network for Lung Segmentation in Phase-Resolved Functional Lung (PREFUL) MRI 

Cristian Crisosto1,2, Andreas Voskrebenzev1,2, Marcel Gutberlet1,3, Filip Klimeš1,2, Frank Wacker1,2, Till Kaireit1,2, Gesa Poeler1,2, Lea Behrendt1,2, Christopher Korz1, and Jens Vogel-Claussen1,2
1Institute of Diagnostic and Interventional Radiology, Hanover Medical School, Hannover, Germany, 2Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), German Center for Lung Research (DZL), Hannover, Germany, 3Biomedical Research in Endstage and 3 Obstructive Lung Disease Hannover (BREATH), German Center for Lung Research (DZL), Hannover, Germany

Translation and establishment of complex pulmonary magnetic resonance (MR) imaging techniques in the clinics requires a reliable, fully automated and fast calculation. In this work we present a semantic convolutional neural network (CNN) model for lung parenchyma and vessel segmentation in combination with parallelized computation on a high-performance computer to design an end-to-end pipeline for phase-resolved functional lung (PREFUL) MRI. The CNN was trained (n=1118) and validated (n=1064) with manually segmented images by a trained radiologist. Automatic segmentation of lung parenchyma was achieved for all tested images.

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