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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