Automated Retinal Layer Segmentation of OCT Images Using Two-Stage FCN and Decision Mask

Optical coherence tomography (OCT) is the standard method of generating high resolution retinal images, which inform retinal disease diagnosis and guide management. However, in order to fully extract and utilize the retinal information from the OCT images, automatic OCT segmentation is essential. Although neural networks have achieved great success with automatic segmentation, only using one neural network model to segment may lead to an ambiguous information problem where the result contains incorrect classification. In this paper, we propose a two-stage fully convolutional network (FCN) method to address these shortcomings. The OCT image is segmented in the first stage via a trained FCN, and in the second stage, the segmentation result is refined via another trained model with a decision mask to improve the segmentation performance. Therefore, two neural network models are trained sequentially to achieve better segmentation performance. The proposed method is evaluated using the publicly available Duke OCT dataset using the F1-score as the metric to measure the performance. The experimental results confirm the improvements of the proposed method.