Automatic longitudinal montaging of adaptive optics retinal images using constellation matching.

Abstract

Adaptive optics (AO) scanning laser ophthalmoscopy offers a non-invasive approach for observing the retina at a cellular level. Its high resolution capabilities have direct application for monitoring and treating retinal diseases by providing quantitative assessment of cone health and density across time. However, accurate longitudinal analysis of AO images requires that AO images from different sessions be aligned, such that cell-to-cell correspondences can be established between timepoints. Such alignment is currently done manually, a time intensive task that is restrictive for large longitudinal AO studies. Automated longitudinal montaging for AO images remains a challenge because the intensity pattern of imaged cone mosaics can vary significantly, even across short timespans. This limitation prevents existing intensity-based montaging approaches from being accurately applied to longitudinal AO images. In the present work, we address this problem by presenting a constellation-based method for performing longitudinal alignment of AO images. Rather than matching intensity similarities between images, our approach finds structural patterns in the cone mosaics and leverages these to calculate the correct alignment. These structural patterns are robust to intensity variations, allowing us to make accurate longitudinal alignments. We validate our algorithm using 8 longitudinal AO datasets, each with two timepoints separated 6-12 months apart. Our results show that the proposed method can produce longitudinal AO montages with cell-to-cell correspondences across the full extent of the montage. Quantitative assessment of the alignment accuracy shows that the algorithm is able to find longitudinal alignments whose accuracy is on par with manual alignments performed by a trained rater.