Unsupervised Learning Model for Registration of Multi-phase Ultra-Widefield Fluorescein Angiography

Abstract

Registration methods based on unsupervised deep learning have achieved good performances, but are often ineffective on the registration of inhomogeneous images containing large displacements. In this paper, we propose an unsupervised learning-based registration method that effectively aligns multi-phase Ultra-Widefield (UWF) fluorescein angiography (FA) retinal images acquired over the time after a contrast agent is applied to the eye. The proposed method consists of an encoder-decoder style network for predicting displacements and spatial transformers to create moved images using the predicted displacements. Unlike existing methods, we transform the moving image as well as its vesselness map through the spatial transformers, and then compute the loss by comparing them with the target image and the corresponding maps. To effectively predict large displacements, displacement maps are estimated at multiple levels of a decoder and the losses computed from the maps are used in optimization. For evaluation, experiments were performed on 64 pairs of early- and late-phase UWF retinal images. Experimental results show that the proposed method outperforms the existing methods.