An Automated Method for Generating Training Sets for Deep Learning based Image Registration

In this paper, we propose an automated method for generating training sets required for realizing deep learning based image registration. The proposed method minimizes effort for supervised learning by automatically generating thousands of training sets from a small number of seed sets, i.e., tens of deformation vector fields obtained with a conventional registration method. To automate this procedure, we solve an inverse problem instead of a direct problem; we produce a floating image by applying a deformation vector field Φ to a reference image and let the inverse vector of Φ be the ground truth for these images. In experiments, the proposed method took 33 minutes to produce 169,890 training sets from approximately 670,000 2-D magnetic resonance (MR) images and 30 seed sets. We further trained GoogLeNet with these training sets and performed holdout validation to compare the proposed method with the conventional registration method in terms of recall and precision. As a result, the proposed method increased recall and precision from 50% to 80%, demonstrating the impact of deep learning for image registration problems.