Artificially-generated consolidations and balanced augmentation increase performance of U-net for lung parenchyma segmentation on MR images
Table 1
Training data split between 4 different categories: Fast Low-Angle Shot (FLASH), Anterior_Posterior_FLASH, balanced steady-state free precession (bSSFP) and Anterior_Posterior_ bSSFP. To balance the training i.e., to relatively obtain the same number of MR images, different factors were chosen. Altogether a total of 17129 MR images with their corresponding 17129 ground truth images were generated for training the CNN.