An optimized volumetric approach to unsupervised image registration

Medical image analysis continues to evolve at an unprecedented rate with the integration of contemporary computer systems. Image registration is fundamental to the task of medical image analysis. Traditional methods of medical image registration are extremely time consuming and at times can be inaccurate. Novel techniques, including the amalgamation of machine learning, have proven to be fast, accurate and reliable. However, supervised learning models are difficult to train due to the lack of ground truth data. Therefore, researchers have endeavoured to explore variant avenues of machine learning, including the implementation of unsupervised learning. In this paper, we continue to explore the use of unsupervised learning for the task of image registration across medical imaging. We postulate that a greater focus on channel-wise data can largely improve model performance. To this end, we employ a sequence generation model, a squeeze excitation network, a convolutional neural network variation of long-short term memory and a spatial transformer network for a channel optimized image registration architecture. To test the proposed approach, we utilize a dataset of 2D brain scans and compare the results against a state-of-the-art baseline model.