Keypoint matching networks for longitudinal fundus image affine registration

Registration of retinal images is an important technique for facilitating the diagnosis and treatment of many eye diseases. Recent studies have shown that deep learning methods can be used for image registration, which is usually faster than conventional registration methods. However, it is not trivial to obtain ground truth for supervised methods and popular unsupervised methods perform not well for retinal images. Therefore, we present a weakly-supervised learning method for affine registration of fundus image. The framework consists of multiple steps, rigid registration, overlap calculation and affine registration. In addition, we introduce a keypoint matching loss to replace common similarity metrics loss used in unsupervised methods. On a fundus image dataset related to multiple eye diseases, our framework can achieve more accurate registration results than that of state-of-the-art deep learning approaches.