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Enhanced feature dynamic fusion gated UNet for robust retinal vessel segmentation
Yang Yang1,2, Yifeng Li1, Jikui Wang3
1Changchun University of Science and Technology, Changchun, China.
Scientific Reports
|December 26, 2025
Summary
This study introduces the Enhanced Feature Dynamic Fusion-Gated U-Net (EFDG-UNet) for accurate retinal vessel segmentation. The model excels at identifying small vessels and handling complex structures, improving diagnostic capabilities.
Area of Science:
- Medical Imaging
- Computer Vision
- Deep Learning
Background:
- Retinal vessel segmentation is crucial for diagnosing eye diseases.
- Existing methods struggle with small vessels, lesions, and multi-scale structures.
Purpose of the Study:
- To develop an advanced deep learning model for enhanced retinal vessel segmentation.
- To improve accuracy in challenging scenarios like low-contrast areas and lesion interference.
Main Methods:
- Proposed the Enhanced Feature Dynamic Fusion-Gated U-Net (EFDG-UNet).
- Incorporated Feature Navigation Hub (FN-Hub) for long-range dependencies.
- Utilized Adaptive Gated Residual Block (AGRB) for dynamic feature selection.
- Employed Parallel Focused Attention Module (PFAM) for fine-grained feature optimization.
Main Results:
- EFDG-UNet achieved state-of-the-art performance on DRIVE, CHASE_DB1, and STARE datasets.
- Attained AUC of 0.9932 and F1-score of 0.8469 on CHASE_DB1.
- Achieved AUC of 0.9886 and F1-score of 0.8412 on DRIVE.
- Demonstrated superior performance in low-contrast regions and complex vessel structures.
Conclusions:
- EFDG-UNet offers a robust solution for retinal vessel segmentation.
- The model's design effectively addresses limitations of previous methods.
- This advancement holds potential for improved ophthalmological diagnostics.
