Background and Objective: Optical coherence tomography (OCT) is a useful technique to monitor retinal layer state both in humans and animal models. Automated OCT analysis in rats is of great relevance to study possible toxic effect of drugs and other treatments before human trials. In this paper, two different approaches to detect the most significant retinal layers in a rat OCT image are presented. Methods: One approach is based on a combination of local horizontal intensity profiles along with a new proposed variant of watershed transformation and the other is built upon an encoder-decoder convolutional network architecture. Results: After a wide validation, an averaged absolute distance error of 3.77 ± 2.59 and 1.90 ± 0.91 µm is achieved by both approaches, respectively, on a batch of the rat OCT database. After a second test of the deep-learning-based method using an unseen batch of the database, an averaged absolute distance error of 2.67 ± 1.25 µm is obtained. The rat OCT database used in this paper is made publicly available to facilitate further comparisons. Conclusions: Based on the obtained results, it was demonstrated the competitiveness of the first approach since outperforms the commercial Insight image segmentation software (Phoenix Research Labs) as well as its utility to generate labelled images for validation purposes speeding significantly up the ground truth generation process. Regarding the second approach, the deep-learning-based method improves the results achieved by the more conventional method and also by other state-of-the-art techniques. In addition, it was verified that the results of the proposed network can be generalized to new rat OCT images.

背景与目的：光学相干断层扫描（OCT）是一种在人类和动物模型中监测视网膜层状态的有用技术。大鼠中的自动OCT分析与在人体试验之前研究药物和其他治疗方法可能产生的毒性作用具有重大意义。在本文中，提出了两种不同的方法来检测大鼠OCT图像中最重要的视网膜层。方法：一种方法是基于局部水平强度分布以及新提出的分水岭变换的组合，另一种方法是基于编码器-解码器卷积网络体系结构。结果：经过广泛的验证，在一批大鼠OCT数据库上，两种方法均分别获得了3.77±2.59和1.90±0.91 µm的平均绝对距离误差。在使用一批看不见的数据库对基于深度学习的方法进行了第二次测试之后，获得的平均绝对距离误差为2.67±1.25 µm。公开使用了本文使用的大鼠OCT数据库，以促进进一步的比较。结论：根据获得的结果，证明了第一种方法的竞争力，因为它优于商业的Insight图像分割软件（Phoenix Research Labs）以及其生成用于验证目的的带标签图像的实用程序，大大加快了地面真相生成过程的速度。关于第二种方法，基于深度学习的方法改进了通过更传统的方法以及其他最新技术所获得的结果。另外，已经证实，所提出的网络的结果可以推广到新的大鼠OCT图像。