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Accurate segmentation of the left ventricle myocardium in cardiac CTangiography (CCTA) is essential for e.g. the assessment of myocardialperfusion. Automatic deep learning methods for segmentation in CCTA mightsuffer from differences in contrast-agent attenuation between training and testdata due to non-standardized contrast administration protocols and varyingcardiac output. We propose augmentation of the training data with virtualmono-energetic reconstructions from a spectral CT scanner which show differentattenuation levels of the contrast agent. We compare this to an augmentation bylinear scaling of all intensity values, and combine both types of augmentation.We train a 3D fully convolutional network (FCN) with 10 conventional CCTAimages and corresponding virtual mono-energetic reconstructions acquired on aspectral CT scanner, and evaluate on 40 CCTA scans acquired on a conventionalCT scanner. We show that training with data augmentation using virtualmono-energetic images improves upon training with only conventional images(Dice similarity coefficient (DSC) 0.895 $\pm$ 0.039 vs. 0.846 $\pm$ 0.125). Incomparison, training with data augmentation using linear scaling improves theDSC to 0.890 $\pm$ 0.039. Moreover, combining the results of both augmentationmethods leads to a DSC of 0.901 $\pm$ 0.036, showing that both augmentationslead to different local improvements of the segmentations. Our results indicatethat virtual mono-energetic images improve the generalization of an FCN usedfor myocardium segmentation in CCTA images.

Improving Myocardium Segmentation in Cardiac CT Angiography using Spectral Information