Joint Optic Disc and Optic Cup Segmentation Based on Adversarial Learning and Guidance Mechanism
Accurate segmentation of the Optic Disc(OD)and Optic Cup(OC)effectively assists in diagnosing and monitoring glaucoma,thereby improving treatment outcomes.However,existing methods do not consider the differences between the various channels of fundus images,making it challenging to achieve accurate segmentation of the OC boundary.To address this problem,a network framework based on adversarial learning and a guidance mechanism,termed ALG-Net,is proposed to improve OD and OC segmentation performance.ALG-Net comprises two main components:a segmentation network and a discriminator.The segmentation network includes a Guidance Fusion Module(GFM)designed to merge single-channel feature information with RGB image features.This allows the network to learn the differences among the various channels of the fundus image,guiding the segmentation network to focus on key regions.The framework also incorporates a discriminator,which encourages the segmentation network to generate more realistic results through adversarial learning.Extensive experimental evaluations are conducted on the REFUGE and Drishti-GS datasets.The results show that ALG-Net achieved balanced accuracies of 98.6％and 95.9％for OD and OC segmentation,respectively,on the RUFUGE dataset and demonstrated better performance on the Drishti-GS dataset.In addition,applying ALG-Net's segmentation results to glaucoma classification tasks yielded an Area Under the ROC Curve(AUC)of 0.983 on the REFUGE dataset,surpassing the classic UNet algorithm by 0.015.This demonstrates ALG-Net's strong support for early diagnosis and monitoring of glaucoma.
万方数据
维普
