An unsupervised deep learning framework for large-scale lung CT deformable image registration

Accurate lung CT deformable image registration is especially useful in many medical image analyzing domains. In this paper, we present a novel unsupervised deep learning framework to speed up registration processing with high accuracy. Our approach consists of a convolutional neural network (CNN) model with frequent connections between layers for extracting robust image features, and of well-designed pre-processing and post-processing techniques to handle with large images without losing the precision. Additionally, during training stage, the local cross coefficient (LCC) and L2-norm for gradients of dense displacement fields (DDF) are adopted to form loss function in the model. Experiments on a large-scale lung CT dataset with each image size of over 400 x 400 x 350 show that our method achieves the best performances on Dice score of 0.9245 and mean squared error (MSE) of 0.0046 compared with some traditional and learning-based methods. Besides, our model has been proved to be robust for various deformations. Above all, our method is several orders of magnitude faster than the state-of-the-art non-learning-based algorithms.