Automatic Segmentation and Intuitive Visualisation of the Epiretinal Membrane in 3D OCT Images Using Deep Convolutional Approaches

Abstract

Epiretinal Membrane (ERM) is a disease caused by a thin layer of scar tissue that is formed on the surface of the retina. When this membrane appears over the macula, it can cause distorted or blurred vision. Although normally idiopathic, its presence can also be indicative of other pathologies such as diabetic macular edema or vitreous haemorrhage. ERM removal surgery can preserve more visual acuity the earlier it is performed. For this purpose, we present a fully automatic segmentation system that can help the clinicians to determine the ERM presence and location over the eye fundus using 3D Optical Coherence Tomography (OCT) volumes. The proposed system uses a convolutional neural network architecture to classify patches of the retina surface. All the 2D OCT slices of the 3D OCT volume of a patient are combined to produce an intuitive colour map over the 2D fundus reconstruction, providing a visual representation of the presence of ERM which therefore facilitates the diagnosis and treatment of this relevant eye disease. A total of 2.428 2D OCT slices obtained from 20 OCT 3D volumes was used in this work. To validate the designed methodology, several representative experiments were performed. We obtained satisfactory results with a Dice Coefficient of 0.826 ± 0.112 and a Jaccard Index of 0.714 ± 0.155, proving its applicability for diagnosis purposes. The proposed system also demonstrated its simplicity and competitive performance with respect to other state-of-the-art approaches.