Optical coherence tomography (OCT) is a powerful imaging tool that is particularly useful for exploring retinal abnormalities in ophthalmological diseases. Recently, it has been used to track changes in the eye associated with neurological diseases such as multiple sclerosis (MS) where certain tissue layer thicknesses have been associated with disease progression. A small percentage of MS patients also exhibit what has been called microcystic macular edema (MME), where uid collections that are thought to be pseudocysts appear in the inner nuclear layer. Very little is known about the cause of this condition so it is important to be able to identify precisely where these pseudocysts occur within the retina. This identi cation would be an important rst step towards furthering our understanding. In this work, we present a detection algorithm to nd these pseudocysts and to report on their spatial distribution. Our approach uses a random forest classi er trained on manual segmentation data to classify each voxel as pseudocyst or not. Despite having a small sample size of ve subjects, the algorithm correctly identi es 84.6% of pseudocysts as compared to manual delineation. Finally, using our method, we show that the spatial distribution of pseudocysts within the macula are generally contained within an annulus around the fovea.