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Diabetic retinal vessel segmentation algorithm based on MA-DUNet
Jian-Zhi Deng1,2, Yan Yang1, Yong-Ping Guo1
1College of Physics and Electronic Information Engineering, Guilin University of Technology, Guilin, China.
Quantitative Imaging in Medicine and Surgery
|July 3, 2025
Summary
This study introduces a novel Multi-modal Attention Deformable U-shaped Network (MA-DUNet) for enhanced retinal vessel segmentation, significantly improving diagnostic accuracy for ocular diseases.
Area of Science:
- Medical Imaging
- Ophthalmology
- Computer Vision
Background:
- Precise retinal vessel segmentation is vital for diagnosing ocular diseases.
- Challenges include complex branching, varying diameters, low contrast, and subtle terminal vessels.
- Accurate segmentation aids early diagnosis and treatment.
Purpose of the Study:
- To improve the accuracy of retinal vessel segmentation.
- To enhance the efficiency of clinical diagnosis through improved segmentation.
Main Methods:
- Proposed the Multi-modal Attention Deformable U-shaped Network (MA-DUNet).
- Incorporated atrous multi-scale (AMS) convolutions for multi-scale feature perception.
- Utilized a gated channel transformation (GCT) attention mechanism for feature transmission.
- Employed a Multi-Modal Attention Fusion Block (MAFB) in the decoding process.
Main Results:
- Achieved high accuracy (95.72%-96.68%) and Area Under the ROC Curve (98.10%-98.89%) on DRIVE, STARE, and CHASE-DB1 datasets.
- Outperformed comparative models in hospital-validated data.
- Demonstrated superior segmentation of fine terminal branches and reduced fragmentation.
Conclusions:
- The MA-DUNet model offers more accurate retinal vessel segmentation.
- It effectively addresses challenges with fine terminal branches, blurred boundaries, and fragmentation.
- Results indicate improved clarity and diagnostic potential for vascular segmentation.
