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Abstract #1972

Artificially-generated Consolidations and Balanced Augmentation increase Performance of U-Net for Lung Parenchyma Segmentation on MR Images

Cristian Crisosto1,2, Andreas Voskrebenzev1,2, Marcel Gutberlet1,2, Filip Klimeš1,2, Till Kaireit1,2, Gesa Pöhler1,2, Tawfik Alsady1,2, Lea Behrendt1,2, Robin Müller1,2, Maximilian Zubke1,2, Frank Wacker1,2, and Jens Vogel-Claussen1,2
1Institute of Diagnostic and Interventional Radiology, Medical School Hannover, Hannover, Germany, 2Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Member of the German Centre for Lung Research (DZL), Hannover, Germany, Hannover, Germany


Accurate fully automated lung segmentation is needed to facilitate Fourier-Decomposition employment-based techniques in clinical routine among different centers. However, the lung parenchyma segmentation remains challenging for convolutional neural networks (CNN) when consolidations are present. To improve training balanced augmentation (BA) and artificially-generated consolidations (AGC) were introduced. The proposed CNN was compared to conventional CNNs without BA and AGC using Sørensen-Dice coefficient (SDC) and Hausdorff coefficient (HD). The SDC / HD of the proposed model is significantly higher (p of 0.0001 and p of 0.0146 / p of 0.0009 and p of 0.0152) when compared to CNNs without BA and AGC.

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