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Emilio López Varela acknowledges its support under FPI Grant Program through PID2019-108435RB-I00 project. [Abstract]: Multiple Sclerosis (MS) is a chronic neurological disease, in which immune-mediated mechanisms lead to pathological processes of neurodegeneration. Optical coherence tomography (OCT) has recently begun to be used to diagnose and monitor patients with MS. Morphological changes in the choroid have been linked to the onset of MS, so an accurate segmentation of this layer is critical. Conventional OCT has several limitations in obtaining accurate images of the choroid, which has been improved through the use of systems such as Enhanced Depth Imaging (EDI) OCT. Unfortunately, many longitudinal studies that have collected samples over the years in the past have been performed using highly variable settings and without the use of the EDI protocol (or similar variants). For these reasons, in this work we propose a series of fully automatic approaches, based on convolutional neural networks, capable of robustly segmenting the choroid in OCT images without using the EDI protocol. To test the robustness and efficiency of our method, we performed experiments on a public dataset and a collected one. The Dice score obtained by the best proposed architecture is 89.7 for the public dataset, and 93.7 for the collected dataset. Instituto de Salud Carlos III; DTS18/00136 Ministerio de Ciencia e Innovación y Universidades; RTI2018-095894-B-I00 Xunta de Galicia; ED431C 2020/24 Ministerio de Ciencia e Innovación; PID2019-108435RB-I00 Axencia Galega de Innovación (GAIN); IN845D 2020/38 Xunta de Galicia; ED431G 2019/01