Non-contrast CT Liver Segmentation Using CycleGAN Data Augmentation from Contrast Enhanced CT

Abstract

Non-contrast CT is often preferred in clinical screening while segmentation of such CT data is more challenging due to the low contrast in tissue boundaries and scarce supervised training data than contrast-enhanced CT (CTce) segmentation. To alleviate manual labelling work of radiologists, we generate training samples for 3D U-Net segmentation network by transforming the existing CTce liver segmentation dataset to the non-contrast CT styled volumes with CycleGAN. We validated the performance of CycleGAN in both unsupervised and hybrid supervised training strategy. The results show that using CycleGAN in unsupervised segmentation can achieve higher mean Dice coefficients than fully supervised manner in liver segmentation. The hybrid training of generated samples and the target task samples can improve the generalization ability of segmentation.