A Novel Edge-Enhanced Networks for Optic Disc and Optic Cup Segmentation

Abstract

Optic disc and optic cup segmentation plays a key role in early diagnosis of glaucoma which is a serious eye disease that can cause damage to the optic nerve, retina, and may cause permanent blindness. Deep learning-based models are used to improve the efficiency and accuracy of fundus image segmentation. However, most approaches currently still have limitations in accurately segmenting optic disc and optic cup, which suffer from the lack of feature abstraction representation and blurring of segmentation in edge regions. This paper proposes a novel edge enhancement network called EE-TransUNet to tackle this challenge. It incorporates the Cascaded Convolutional Fusion block before each decoder layer. This enhances the abstract representation of features and preserves the information of the original features, thereby improving the model's nonlinear fitting ability. Additionally, the Channel Shuffling Multiple Expansion Fusion block is incorporated into the skip connections of the model. This block enhances the network's ability to perceive and characterize image features, thereby improving segmentation accuracy at the edges of the optic cup and optic disc. We validate the effectiveness of the method by conducting experiments on three publicly available datasets, RIM-ONE-v3, REFUGUE and DRISHTI-GS. The Dice coefficients on the test set are 0.871, 0.9056, 0.9068 for the optic cup region and 0.9721, 0.967, 0.9774 for the optic disc region, respectively. The proposed method achieves competitive results compared to other state-of-the-art methods. Our code is available at: https://github.com/wangyunyuwyy/EE-TransUNet.