Deep Learning for Segmentation of Optic Disc and Retinal Layers in Peripapillary Optical Coherence Tomography Images

Optical coherence tomography (OCT) is a non-invasive technique that allows the retina to be studied with precision, the analysis of the features of its layers and other structures such as the macula or the optic nerve. This is why it is used in the diagnosis and monitoring of eye diseases such as glaucoma and optic neuritis. A crucial step in this process is the segmentation of the different layers, which is a great challenge due to its complexity. In this work, a methodology based on deep learning and transfer learning will be developed to automatically segment nine retinal layers in OCT images centred on the optic disc. In addition, the thickness of each retinal layer will be measured along each B-scan. For this purpose, OCT images from a public dataset and a dataset collected from depth-enhanced images will be used. The proposed method achieves a Dice score of 83.6%, similar to that obtained in the state of the art, segmenting the nine retinal layers and the optic disc in both sets of images. In addition, the different layers are represented in three different graphical formats.