In ophthalmologic examinations, optical coherence tomography (OCT) images are used to measure thickness of retinal layers that is a significant criterion to diagnose ophthalmopathy. Since itis difficult even for experts to manually measure the thickness from OCT images, retinal layer segmentation methods [1, 2] have been investigated to detect retinal layers from OCT images. Due to lack of labeled training data, these methods cannot deal with a variety of retinal shapes. Furthermore, these methods may falsely segment background noise as retinal layers. To address the above problems, in this paper, we pro- pose two data augmentation methods: formula-driven data augmentation (FDDA) and partial retinal layer copying (PRLC). FDDA emulates a variety of retinal shapes based on mathematical formulas, and increases the variability of retinal shapes in the training data. PRLC duplicates a part of retinal layers and overlays it on the background region as noise, and reduces false segmentation in the background region. Through a set of experiments using public datasets, we demonstrate the effectiveness of the proposed methods.