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.
@article{arxiv.1804.11024,
title = {Adversarial Image Registration with Application for MR and TRUS Image Fusion},
author = {Pingkun Yan and Sheng Xu and Ardeshir R. Rastinehad and Brad J. Wood},
journal= {arXiv preprint arXiv:1804.11024},
year = {2018}
}
Comments
Presented at the workshop on MLMI 2018, LNCS, volume 11046, pages 197 to 204