Improving Myocardium Segmentation in Cardiac CT Angiography using Spectral Information

TL;DR

This paper demonstrates that augmenting training data with spectral CT-derived virtual mono-energetic images enhances deep learning-based myocardium segmentation in cardiac CT, improving generalization across different scanner protocols.

Contribution

The study introduces the use of spectral CT virtual mono-energetic images for data augmentation to improve deep learning segmentation robustness in CCTA.

Findings

Virtual mono-energetic augmentation improves DSC to 0.895

Linear scaling augmentation improves DSC to 0.890

Combining both augmentations yields DSC of 0.901

Abstract

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.