  Accurate segmentation of the left ventricle myocardium in cardiac CT
angiography (CCTA) is essential for e.g. the assessment of myocardial
perfusion. Automatic deep learning methods for segmentation in CCTA might
suffer from differences in contrast-agent attenuation between training and test
data due to non-standardized contrast administration protocols and varying
cardiac output. We propose augmentation of the training data with virtual
mono-energetic reconstructions from a spectral CT scanner which show different
attenuation levels of the contrast agent. We compare this to an augmentation by
linear scaling of all intensity values, and combine both types of augmentation.
We train a 3D fully convolutional network (FCN) with 10 conventional CCTA
images and corresponding virtual mono-energetic reconstructions acquired on a
spectral CT scanner, and evaluate on 40 CCTA scans acquired on a conventional
CT scanner. We show that training with data augmentation using virtual
mono-energetic images improves upon training with only conventional images
(Dice similarity coefficient (DSC) 0.895 $\pm$ 0.039 vs. 0.846 $\pm$ 0.125). In
comparison, training with data augmentation using linear scaling improves the
DSC to 0.890 $\pm$ 0.039. Moreover, combining the results of both augmentation
methods leads to a DSC of 0.901 $\pm$ 0.036, showing that both augmentations
lead to different local improvements of the segmentations. Our results indicate
that virtual mono-energetic images improve the generalization of an FCN used
for myocardium segmentation in CCTA images.
