No abstract

No abstract

No abstract

No abstract

Deep learning-based techniques can obtain high precision for multimodal stroke segmentation tasks. However, the performance often requires a large number of training examples. Additionally, existing data extension approaches for the segmentation are less efficient in creating much more realistic images. To overcome these limitations, an unsupervised adversarial data augmentation mechanism (UTC-GAN) is developed to synthesize multimodal computed tomography (CT) brain scans. In our approach, the CT samples generation and cross-modality translation differentiation are accomplished simultaneously by integrating a Siamesed auto-encoder architecture into the generative adversarial network. In addition, a Gaussian mixture translation module is further proposed, which incorporates a translation loss to learn an intrinsic mapping between the latent space and the multimodal translation function. Finally, qualitative and quantitative experiments show that UTC-GAN significantly improves the generation ability. The stroke dataset enriched by the proposed model also provides a superior improvement in segmentation accuracy, compared with the performance of current competing unsupervised models.