Related Experiment Video
Updated: Apr 11, 2026
Evaluation of Capillary and Other Vessel Contribution to Macular Perfusion Density Measured with Optical Coherence Tomography Angiography
Published on: February 18, 2022
Segmentation of microcystic macular edema in Cirrus OCT scans with an exploratory longitudinal study
Emily K Swingle1, Andrew Lang2, Aaron Carass3
1Department of Biomedical Engineering, The Ohio State University.
Abstract:
Microcystic macular edema (MME) is a term used to describe pseudocystic spaces in the inner nuclear layer (INL) of the human retina. It has been noted in multiple sclerosis (MS) as well as a variety of other diseases. The processes that lead to MME formation and their change over time have yet to be explained sufficiently. The low rate at which MME occurs within such diverse patient groups makes the identification and consistent quantification of this pathology important for developing patient-specific prognoses. MME is observed in optical coherence tomography (OCT) scans of the retina as changes in light reflectivity in a pattern suggestive of fluid accumulations called pseudocysts. Pseudocysts can be readily identified in higher signal-to-noise ratio (SNR) images, however pseudocysts can be indistinguishable from noise in lower SNR scans. In this work, we expand upon our earlier MME identification methods on Spectralis OCT scans to handle lower quality Cirrus OCT scans. Our approach uses a random forest classifier, trained on manual segmentation of ten subjects, to automatically detect MME. The algorithm has a true positive rate for MME identification of 0.95 and a Dice score of 0.79. We include a preliminary longitudinal study of three patients over four to five years to explore the longitudinal changes of MME. The patients with relapsing-remitting MS and neuromyelitis optica appear to have dynamic pseudocyst volumes, while the MME volume appears stable in the one patient with primary progressive MS.
Insights
Microcystic macular edema (MME) detection in optical coherence tomography (OCT) scans is improved with a new random forest algorithm. This method accurately identifies MME in lower quality scans, aiding in patient prognosis.
Area of Science:
- Ophthalmology
- Medical Imaging
- Neurology
Background:
- Microcystic macular edema (MME) presents as pseudocystic spaces in the retina's inner nuclear layer (INL).
- MME is associated with multiple sclerosis (MS) and other diseases, but its formation and progression are poorly understood.
- Accurate MME identification and quantification are crucial for patient prognoses due to its low occurrence across diverse conditions.
Purpose of the Study:
- To develop and validate an automated method for detecting MME in optical coherence tomography (OCT) scans, particularly in lower signal-to-noise ratio (SNR) images.
- To adapt existing MME identification algorithms for use with Cirrus OCT scans, expanding their applicability.
- To investigate the longitudinal changes of MME in patients with neurological conditions.
Main Methods:
- A random forest classifier was trained using manual segmentation of MME in ten subjects.
- The algorithm was specifically adapted to process lower quality Cirrus OCT scans, building upon prior work with Spectralis OCT.
- A preliminary longitudinal study tracked MME changes in three patients over 4-5 years.
Main Results:
- The automated MME detection algorithm achieved a true positive rate of 0.95 and a Dice score of 0.79.
- The method successfully identified MME in lower quality Cirrus OCT scans.
- Longitudinal analysis indicated dynamic pseudocyst volumes in patients with relapsing-remitting MS and neuromyelitis optica, contrasting with stable MME volume in a primary progressive MS patient.
Conclusions:
- The developed random forest classifier provides an effective and automated approach for MME detection in OCT scans, even in lower quality images.
- This advancement facilitates more consistent quantification of MME, supporting improved patient prognoses.
- Preliminary longitudinal data suggest distinct MME progression patterns based on MS subtype and related neurological conditions.
