Adversarial Image Registration with Application for MR and TRUS Image Fusion
This work addresses a critical need for robust and accurate image fusion in clinical applications like prostate biopsy, though it appears incremental as it builds on existing adversarial methods for registration.
The paper tackled the challenging problem of aligning multimodal medical images, specifically MR and TRUS images for prostate interventions, by proposing an adversarial image registration framework that achieved promising results on clinical datasets.
Robust and accurate alignment of multimodal medical images is a very challenging task, which however is very useful for many clinical applications. For example, magnetic resonance (MR) and transrectal ultrasound (TRUS) image registration is a critical component in MR-TRUS fusion guided prostate interventions. However, due to the huge difference between the image appearances and the large variation in image correspondence, MR-TRUS image registration is a very challenging problem. In this paper, an adversarial image registration (AIR) framework is proposed. By training two deep neural networks simultaneously, one being a generator and the other being a discriminator, we can obtain not only a network for image registration, but also a metric network which can help evaluate the quality of image registration. The developed AIR-net is then evaluated using clinical datasets acquired through image-fusion guided prostate biopsy procedures and promising results are demonstrated.