Artificially-generated consolidations and balanced augmentation increase performance of U-net for lung parenchyma segmentation on MR images
Fig 4
Exemplary segmentation results of CNNUnbal/NoCons, CNNBal/NoCons and CNNBal/Cons for two patients with CF.
The first column represents the morphology image, the second column the manual segmentation, the third column the CNN segmentation after postprocessing. The fourth column represents the output of the CNN, the probability matrix. The fifth column represents the difference between manual and CNN segmentation.