Automated segmentation of the eye's morphological features in OCT datasets is fundamental to support rapid clinical decision making and to avoid time-consuming manual segmentation of the images. In recent years, deep learning (DL) techniques have become a commonly employed approach to tackle image analysis problems. This study provides a description of the development of automated DL segmentation methods of the Bruch's membrane opening (BMO) from a series of OCT cross-sectional scans. A range of DL techniques are systematically evaluated, with the secondary goal to understand the effect of the network input size on the model performance. The results indicate that a fully semantic approach, in which the whole B-scan is considered with data augmentation, results in the best performance, achieving high levels of similarity metrics with a dice coefficient of 0.995 and BMO boundary localization with a mean absolute error of 1.15 pixels. The work further highlights the importance of fully semantic methods over patch-based techniques in the classification of OCT regions.