Patch-based CNN for corneal segmentation of AS-OCT images: Effect of the number of classes and image quality upon performance

Abstract

Anterior segment optical coherence tomography (AS-OCT) is a fundamental ophthalmic imaging technique. AS-OCT images can be examined by experts and segmented to provide quantitative metrics that inform clinical decision making. Manual segmentation of these images is time-consuming and subjective, encouraging software developers in the field to automate segmentation procedures. Traditional programing segmentation approaches are being replaced by deep learning methods, which have shown state-of-the-art performance in AS-OCT image analysis. In this study, a method based on patch-based convolutional neural networks (CNN) was used to segment the three main boundaries of the cornea: the epithelium, Bowman's layer, and the endothelium. To assess the effect of the number of classes on performance, the model was designed as a patch-based boundary classifier using 4 and 8 classes. The effect of image quality was also assessed using different data distributions during the training process. While the Dice coefficient and probability revealed greater precision for the 8 class models, the boundary error metric indicated comparable performance. Additionally, for 8 class models, the image quality test had only a small negative effect on performance, which may be an indication of the robustness of the model and could also suggest that the data augmentation methods did not show significant improvement. These findings contribute to the development of automatic segmentation techniques for AS-OCT images, since patch-based methods have been largely unexplored in favor of other deep learning techniques. The overall performance of the proposed method is comparable to other well-established segmentation methods.