In pharmaceutical research, optical coherence tomography (OCT) has been used for the assessment of diseases such as age-related macular degeneration (AMD) and retinal pigment epithelial (RPE) atrophy on animals in pre-clinical studies. To measure the thickness of the total retina and individual retina layers on these OCT images, it is necessary to perform accurate segmentation which is known to be a labor-intensive and error-prone task especially on images of diseased animals with significant retina distortion. Herein we elect to perform automated segmentation of retina layers on the OCT images of rodent subjects using deep convolutional neural networks (CNN). Based on a U-Net architecture, we perform segmentation of three most important retina layers using U-Net CNN models trained with three different strategies: Training from scratch, transfer learning, and continued training from a pre-trained model of a different animal cohort. To compare the three strategies, three models are trained and tested on OCT scans of rodent subjects, and the segmentation results are compared with manually corrected delineations using Dice similarity coefficient (DSC) as a measure of accuracy. Results show that although all three strategies lead to similar performance, transfer learning and continued training are effective in accelerating the training process, while continued training manages to generate the most accurate results that are also the most plausible via visual inspections.