The recent application of deep learning technologies in medical image registration has exponentially decreased the registration time and gradually increased registration accuracy when compared to their traditional counterparts.
Image-guided interventions are saving the lives of a large number of patients where the image registration problem should indeed be considered as the most complex and complicated issue to be tackled.
To this end, these two components are tackled in an end-to-end manner via reinforcement learning in this work.
Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of development in medical image registration using deep learning.
This is achieved by unsupervised domain adaptation in the registration process and allows for easier application to different datasets without extensive retraining. To achieve our objective we train a network that transforms the given input image pair to a latent feature space vector using autoencoders.
We present recursive cascaded networks, a general architecture that enables learning deep cascades, for deformable image registration.
Mutual information (MI) is the standard method used in image registration and the most studied one but can diverge and produce wrong results when used in an automated manner.
We then embed the KLDivNet into a registration network to achieve the unsupervised deformable registration for multi-modality images.
We tackle biomedical image segmentation in the scenario of only a few labeled brain MR images.