关键词: 青光眼诊断, 深度学习, 视杯视盘分割, 多尺度特征融合, 注意力机制

Abstract: To address the challenge of accurate joint segmentation of the Optic Cup (OC) and Optic Disc (OD) in early glaucoma diagnosis due to blurred boundaries and varied morphologies, a lightweight multi-scale feature enhancement network (LFM-Net) was proposed. This network employs an encoder-decoder architecture, aiming to improve segmentation accuracy by enhancing multi-scale feature representation and cross-layer feature fusion capabilities. Specifically, in the encoder stage, global contextual features were extracted layer by layer using depthwise separable convolutions and inverted bottleneck structures. A Multi-Scale Feature enhancement Aggregation (MSFA) module was introduced, utilizing its multi-branch convolutions and channel attention mechanisms to adaptively capture and aggregate global contextual and local detail features while maintaining low computational cost, thus addressing the significant differences in optic cup and optic disc sizes. In the decoder stage, a Convolutional Attention Feature Fusion Module (CAFM Fusion) was designed, combining 3D convolutional attention and pixel attention mechanisms to optimize feature transfer in skip connections, effectively suppressing background noise and sharpening edge responses, ultimately achieving efficient fusion of cross-layer features. Experimental results on three publicly available fundus image datasets—REFUGE, DRISHTI-GS, and RIM-ONE-r3—show that LFM-Net outperforms comparable methods such as U-Net, and TransUnet in key metrics including Dice coefficient, Intersection over Union (IoU), and accuracy. While maintaining lightweight design, LFM-Net accurately extracts OC and OD features, achieving high-precision segmentation. Furthermore, it demonstrates strong generalization ability across different datasets, providing effective technical support for computer-aided diagnosis of glaucoma.

Key words: glaucoma diagnosis, deep learning, optic cup and disc segmentation, multi-scale feature fusion, attention mechanism