Synthesis of Registered Multimodal Medical Images with Lesions

The collection and annotation of medical images data have always been a challenge in many data-driven medical image processing tasks, especially for registered multimodal medical images data. This can be effectively alleviated by utilizing the image synthesis technology. However, directly-synthesized medical images generated by current methods usually have unreasonable structures or contours and uncontrollable lesions. In this paper, we proposed a new method to synthesize registered multimodal medical images from a random normal distribution matrix based on the Generative Adversarial Networks. Besides, the corresponding lesions can be generated efficiently based on the selected lesion labels. We performed validation experiments on multiple public datasets to verify the effectiveness of synthetic lesions and the availability of synthetic data. The results show that our synthetic data can be used as pre-trained data or enhanced data in medical image intelligent processing tasks to greatly improve the generalization ability of the model.