*Proceeding Paper* **Automatic Segmentation and Visualisation of the Epirretinal Membrane in OCT Scans Using Densely Connected Convolutional Networks †**

**Mateo Gende 1,2,\*, Joaquim de Moura 1,2, Jorge Novo 1,2, Pablo Charlón 3,4 and Marcos Ortega 1,2**


**Abstract:** The Epiretinal Membrane (ERM) is an ocular disease that appears as a fibro-cellular layer of tissue over the retina, specifically, over the Inner Limiting Membrane (ILM). It causes vision blurring and distortion, and its presence can be indicative of other ocular pathologies, such as diabetic macular edema. The ERM diagnosis is usually performed by visually inspecting Optical Coherence Tomography (OCT) images, a manual process which is tiresome and prone to subjectivity. In this work, we present a methodology for the automatic segmentation and visualisation of the ERM in OCT volumes using deep learning. By employing a Densely Connected Convolutional Network, every pixel in the ILM can be classified into either healthy or pathological. Thus, a segmentation of the region susceptible to ERM appearance can be produced. This methodology also produces an intuitive colour map representation of the ERM presence over a visualisation of the eye fundus created from the OCT volume. In a series of representative experiments conducted to evaluate this methodology, it achieved a Dice score of 0.826 ± 0.112 and a Jaccard index of 0.714 ± 0.155. The results that were obtained demonstrate the competitive performance of the proposed methodology when compared to other works in the state of the art.

**Keywords:** epiretinal membrane; machine learning; medical diagnostic imaging; optical coherence tomography
