Quantification of Hypotony Maculopathy Using Spectral-Domain Optical Coherence Tomography

We provide a free-to-use, open-source algorithm to quantify macular hypotony based on optical coherence tomography (OCT) images. This numerical approach calculates a metric that measures the deviations of Bruch's membrane from a smooth ideal retinal layer.

Hypotony maculopathy is a recurrent complication of glaucoma surgical interventions in which extremely low intraocular pressure triggers changes in the shape of retinal layers. Abnormal folds can often be observed in the retina using standard fundoscopy, but OCT is particularly important to appreciate the severity of symptoms at different depths. Despite the need for metrics that could be used for the informed clinical decision to evaluate the progression and resolution of macular hypotony, algorithms that quantify the retinal folds are not available in the literature or included in clinical imaging equipment. The purpose of this work is to introduce a simple algorithm that can be used to assess hypotony maculopathy from OCT B-Scans and volumes and a free, open-source implementation.

The pipeline we present is based on a straightforward segmentation of Bruch's membrane complex. The principal idea of quantification is to compute a smoothed version of this complex and analyze the deviations from an ideal interface. Such deviations are then measured and added to create a metric that characterizes each OCT B-Scan. A full OCT volume reconstruction is thus characterized by the average metric obtained from all planes.

We tested the metric we proposed against the assessment of 3 experts and obtained a very good correspondence, with Pearson correlation coefficients higher than 0.8. Furthermore, agreement with automatic analysis seemed better than between experts. We describe the pipeline in detail and illustrate the results with a group of patients, comparing baseline images, severe hypotony maculopathy, and a variety of outcomes.

The tool we introduce and openly provide fills a clinical gap to quantitatively grade hypotony maculopathy. It offers a metric of relatively simple interpretation that can be used to help clinicians in cases where the regression of symptoms is not obvious to the naked eye. Our pilot study demonstrates reliable results, and an open-source implementation facilitates easy improvements to our algorithm.