Longitudinal Matching of in vivo Adaptive Optics Images of Fluorescent Cells in the Human Eye Using Stochastically Consistent Superpixels

摘要

Fluorescence microscopy has transformed our understanding of modern biology. Recently, this technology was translated to the clinic using adaptive optics enhanced indocyanine green ophthalmoscopy, which enables retinal pigment epithelial cells to be fluorescently-labeled and imaged in the living human eye. Monitoring these cells across longitudinal images on the time scale of months is important for understanding blinding diseases, but remains challenging due to inherent eye-motion-caused distortions, substantial visit-to-visit image displacements, and weak cell boundaries due to the nature of fluorescence data. This paper introduces a stochastically consistent superpixel method to address these issues. First, large displacement optical flow is estimated by embedding global image displacements from a set of maximal stable extremal regions into a variational framework. Next, optical flow is utilized to initialize bilateral Gaussian processes that model superpixel movements. Finally, a generative probabilistic framework is developed to create consistent superpixels constrained with maximal likelihood criterion. Consistent superpixels were evaluated on images from 11 eyes which were longitudinally imaged over 3-12 months. Validation datasets revealed high accuracy across time points despite the presence of visit-to-visit changes.