Generative Adversarial Networks And Domain Adaptation For Training Data Independent Image Registration
This work addresses the need for more adaptable medical image registration tools that reduce retraining efforts for clinicians and researchers, though it is incremental as it builds on existing GAN and domain adaptation techniques.
The paper tackles the problem of deep learning-based medical image registration methods not generalizing well to images from different scanners or anatomies, and presents a domain adaptation approach that achieves better registration performance on unseen datasets, outperforming conventional methods.
Medical image registration is an important task in automated analysis of multi-modal images and temporal data involving multiple patient visits. Conventional approaches, although useful for different image types, are time consuming. Of late, deep learning (DL) based image registration methods have been proposed that outperform traditional methods in terms of accuracy and time. However,DL based methods are heavily dependent on training data and do not generalize well when presented with images of different scanners or anatomies. We present a DL based approach that can perform medical image registration of one image type despite being trained with images of a different type. 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. The resultant encoded feature space is used to generate the registered images with the help of generative adversarial networks (GANs). This feature transformation ensures greater invariance to the input image type. Experiments on chest Xray, retinal and brain MR images show that our method, trained on one dataset gives better registration performance for other datasets, outperforming conventional methods that do not incorporate domain adaptation