Training data independent image registration using generative adversarial networks and domain adaptation. (April 2020)

• Record Type: Journal Article
• Authors: Mahapatra, Dwarikanath; Ge, Zongyuan
• Is Part Of: Pattern recognition. Volume 100(2020:Apr.)
• Publication Date: 2020-04
• Subjects: Registration -- Domain adaptation -- GANs -- X-ray -- MRI; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.107109
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A dataset independent deep learning based approach for image registration is described. Unsupervised domain adaptation techniques through latent space feature encoding is described. Registration network trained on lung X-ray images is used to register brain MRI and retinal images. Abstract: Medical image registration is an important task in automated analysis of multimodal 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 X-ray, 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.