Byungchul Tak, Shu Tao, et al.
IC2E 2016
Conventional approaches to image registration consist of time consuming iterative methods. Most current deep learning (DL) based registration methods extract deep features to use in an iterative setting. We propose an end-to-end DL method for registering multimodal images. Our approach uses generative adversarial networks (GANs) that eliminates the need for time consuming iterative methods, and directly generates the registered image with the deformation field. Appropriate constraints in the GAN cost function produce accurately registered images in less than a second. Experiments demonstrate their accuracy for multimodal retinal and cardiac MR image registration.
Byungchul Tak, Shu Tao, et al.
IC2E 2016
Kevin Gu, Eva Tuecke, et al.
ICML 2024
Zongyuan Ge, Sergey Demyanov, et al.
BMVC 2017
Kristjan Greenewald, Yuancheng Yu, et al.
NeurIPS 2024