  In this work, we propose a self-supervised learning method for affine image
registration on 3D medical images. Unlike optimisation-based methods, our
affine image registration network (AIRNet) is designed to directly estimate the
transformation parameters between two input images without using any metric,
which represents the quality of the registration, as the optimising function.
But since it is costly to manually identify the transformation parameters
between any two images, we leverage the abundance of cheap unlabelled data to
generate a synthetic dataset for the training of the model. Additionally, the
structure of AIRNet enables us to learn the discriminative features of the
images which are useful for registration purpose. Our proposed method was
evaluated on magnetic resonance images of the axial view of human brain and
compared with the performance of a conventional image registration method.
Experiments demonstrate that our approach achieves better overall performance
on registration of images from different patients and modalities with 100x
speed-up in execution time.
