  Background: The clinical utility of late gadolinium enhancement (LGE) cardiac
MRI is limited by the lack of standardization, and time-consuming
postprocessing. In this work, we tested the hypothesis that a cascaded deep
learning pipeline trained with augmentation by synthetically generated data
would improve model accuracy and robustness for automated scar quantification.
  Methods: A cascaded pipeline consisting of three consecutive neural networks
is proposed, starting with a bounding box regression network to identify a
region of interest around the left ventricular (LV) myocardium. Two further
nnU-Net models are then used to segment the myocardium and, if present, scar.
The models were trained on the data from the EMIDEC challenge, supplemented
with an extensive synthetic dataset generated with a conditional GAN.
  Results: The cascaded pipeline significantly outperformed a single nnU-Net
directly segmenting both the myocardium (mean Dice similarity coefficient (DSC)
(standard deviation (SD)): 0.84 (0.09) vs 0.63 (0.20), p < 0.01) and scar (DSC:
0.72 (0.34) vs 0.46 (0.39), p < 0.01) on a per-slice level. The inclusion of
the synthetic data as data augmentation during training improved the scar
segmentation DSC by 0.06 (p < 0.01). The mean DSC per-subject on the challenge
test set, for the cascaded pipeline augmented by synthetic generated data, was
0.86 (0.03) and 0.67 (0.29) for myocardium and scar, respectively.
  Conclusion: A cascaded deep learning-based pipeline trained with augmentation
by synthetically generated data leads to myocardium and scar segmentations that
are similar to the manual operator, and outperforms direct segmentation without
the synthetic images.
