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Abstract #1720

Deep Learning Assisted Fully Automatic Post-Processing for Quantitative Lung MRI

Andreas Max Weng1, Christian Kestler1, Andreas Steven Kunz1, Simon Veldhoen1, Thorsten Alexander Bley1, Herbert Köstler1, and Tobias Wech1

1Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany

Functional lung MRI still suffers from a time consuming post-processing with manual image segmentation being its most time consuming part. We introduce and evaluate a deep learning based semantic image segmentation technique to enable fully automated post-processing in SENCEFUL-MRI. Obtained segmentations were compared to manual segmentations using the DICE similarity coefficient (DSC). Furthermore, quantitative ventilation values were obtained after manual and automatic segmentation. Mean DSC of the binary segmentation masks was 0.83 ± 0.09 and no significant difference in quantitative ventilation values was observed. Obtained results show that the time consuming manual post-processing in functional lung MRI can be automated by the proposed neural network.

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