Automatic modeling and classification of vitreomacular traction pathology stages

Abstract

Retinal pathologies that are detected too late and/or left untreated can seriously damage eyesight. It is important to monitor the retina and react to any pathological changes. A fast, accurate, non-invasive, and even three-dimensional retina examination is the optical coherence tomography (OCT). In this paper we propose a new automated classification method for evaluation of vitreomacular interface (VRI) in human eyes. We present an approach for modelling changes in retina structure during the progression of vitreomacular traction (VMT) pathology. Presented experiments were performed on volumetric data acquired from adult patients with the use of Avanti RTvue device. Advanced digital image processing algorithms were subsequently applied to each OCT cross-section (B-scan) for image denoising and flattening, as well as retina layers segmentation. The proposed solution has a good accuracy and almost all subjects were successfully classified into one of 4 groups corresponding to various stages of VMT. The developed models of VMT stages show a high potential of the proposed method to support ophthalmologists in making appropriate clinical decisions.