Abstract
Optical coherence tomography is one of the key diagnostic methods used in ophthalmology and has a high potential for application in an automatic analysis. In this study, we collected, annotated, and analyzed 44 three-dimensional optical coherence tomography images obtained in 39 patients suffering from central serous chorioretinopathy. A semantic annotation of the pathological changes includes three classes: (1) retinal neuroepithelial detachment, (2) retinal pigment epithelium alteration, and (3) a leakage zone. Machine learning methods have been applied to distinguish classes 2 and 3 based on the brightness characteristics of optical coherent tomography images. Intra-group clustering of the class instances showed that the separation of two groups of changes in class 2 can be associated with differences in the volumetric characteristics, whereas the brightness characteristics in class 3 differ significantly depending on the age of the patients, which can be used to predict the course of the disease.
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