Unsupervised Learning for Deformable Medical Image Registration

Abstract

Image registration is a fundamental task in a variety of medical imaging studies and clinical image analysis such as comparison of patient data with anatomical structures. In order to solve the problems of conventional image registration approaches such as high computational time, recent deep learning supervised and unsupervised methods have been extensively studied due to its excellence performance and fast computational time. In this study, we propose a deep learning-based network for deformable medical image registration using unsupervised learning. In this paper, we solve the image registration optimization problem by modelling a function using convolutional neural network with polyphase decomposition to learn the spatial transformable parameters based on the inputted images and generate the registration field. A spatial transformer is used to reconstruct the output warped image while imposing smoothness constraints on the registration field. With polyphase decomposition, our proposed method learns more features based on the inputted image pairs without the need for any ground-truth registration field. Experimental results using 3D T1 Brain MRI volume scans and comparing with a state-of-the-art image registration methods demonstrated that our method provides a better 3D image registration. Our proposed method utilizes less computational time in registering unseen pairs of input images during inference and can be applied for other unimodal image registration tasks and the hyper-parameters can be adjusted for the specific task.