Algorithmic statistics of retinal images

2026-08-06

AI Summary

This study addresses the issue of physiologically irrelevant systematic distortions introduced by existing non-metric methods when analyzing three-dimensional retinal OCT images, which can compromise the assessment of disease progression. The authors propose a metric learning framework that, for the first time, integrates Normalized Compression Distance (NCD) with anisotropic structure-enhancement filtering to construct interpretable and metrically consistent Normalized Compression Vector (NCV) representations. This approach effectively reveals category-dependent statistical distortions potentially induced by non-metric embeddings. Experimental results demonstrate that NCV predicts visual field functional changes with an error of approximately 0.5 dB, outperforming current non-metric deep learning methods. Furthermore, the framework’s capacity to quantify and visualize structural differences is validated in both human glaucoma patients and non-human primate models.

Abstract

There has been a tremendous amount of image processing and machine learning research to measure and classify disease progression from live optical coherence tomography (OCT) imaging of the retina. The images considered here are large, complex, three-dimensional (3-D) and difficult to visualize effectively. Many current supervised machine learning approaches, \emph{e.g.} neural networks, are non-metric meaning that any features or measurements generated can introduce systematic distortion that may be correlated with underlying non-meaningful physiological differences. Here we present a metric learning approach using the normalized compression distance (NCD) combined with anisotropic structure-enhancing filters to quantify and visualize the principal differences among a collection of 3-D retinal images. We validate the NCD-measured structural differences between pairs of images against the physician-measured change in visual field function, achieving a prediction error of $\sim$ 0.5 dB, more accurate than non-metric deep learning approaches. The normalized compression vectors (NCV) are proposed as a feature set measuring visual differences among a collection of 3-D microscopy images. The utility of the NCV for visualizing and measuring patterns of change is demonstrated for a human with moderate non-progressing glaucoma and for a non-human primate model using intraocular pressure setting manipulation. We conclude with a brief simulation of non-metric embedding features, \emph{e.g.} from neural networks, introducing class-correlated statistical distortion.

Problem

• retinal imaging
• metric learning
• structural distortion
• OCT
• non-metric methods

Innovation

• metric learning
• normalized compression distance
• anisotropic filtering
• retinal OCT imaging
• compression-based features