REMPE: Registration of Retinal Images through Eye Modelling and Pose Estimation

Abstract

Objective: In-vivo assessment of small vessels can promote accurate diagnosis and monitoring of diseases related to vasculopathy, such as hypertension and diabetes. The eye provides a unique, open, and accessible window for directly imaging small vessels in the retina with non-invasive techniques, such as fundoscopy. In this context, accurate registration of retinal images is of paramount importance in the comparison of vessel measurements from original and follow-up examinations, which is required for monitoring the disease and its treatment. At the same time, retinal registration exhibits a range of challenges due to the curved shape of the retina and the modification of imaged tissue across examinations. Thereby, the objective is to improve the state-of-the-art in the accuracy of retinal image registration.

Method: In this work, a registration framework that simultaneously estimates eye pose and shape is proposed. Corresponding points in the retinal images are utilized to solve the registration as a 3D pose estimation.

Results: The proposed framework is evaluated quantitatively and shown to outperform state-of-the-art methods in retinal image registration for fundoscopy images.

Conclusion: Retinal image registration methods based on eye modelling allow to perform more accurate registration than conventional methods.

Significance: This is the first method to perform retinal image registration combined with eye modelling. The method improves the state-of-the-art in accuracy of retinal registration for fundoscopy images, quantitatively evaluated in benchmark datasets annotated with ground truth. The implementation of registration method has been made publicly available.