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.