Deep learning-based image registration method: with application to Scanning Laser Ophthalmoscopy (SLO) longitudinal images

This work reports a deep-learning based registration algorithm that aligns scanning laser ophthalmoscopy (SLO) retinal images collected from a longitudinal pre-clinical animal study. We address the problem of determining correspondences between two retinal images in agreement with a geometric model such as an homography or thin-plate spline (TPS) transformation, and estimating its parameters. The contributions of this work are two-fold. First, we propose a convolutional neural network architecture for retinal image registration based on geometric models. The architecture is based on three main components that mimic the standard steps of feature extraction, matching and model parameter estimation, while being trainable end-to-end. Second, we demonstrate that the network parameters can be trained from synthetically generated imagery without the need for manual annotation and that our matching layer significantly increases generalization capabilities to never-seen-before images. Overall, for mono-modality longitudinal registration, the deep-learning registration method achieved mean error in the range of 18.93 ± 0.51 µm (Hom), 26.01 ± 0.84 µm (TPS) and 39.30 ± 2.04 µm (TPS+Hom).