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.

Publication
IEEE Journal of Biomedical and Health Informatics, 2020

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