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TCDDU-Net: combining transformer and convolutional dual-path decoding U-Net for retinal vessel segmentation.
Nianzu Lv1, Li Xu2, Yuling Chen3
1College of Information Engineering, Xinjiang Institute of Technology, No.1 Xuefu West Road, Aksu, 843100, Xinjiang, China.
Scientific Reports
|October 30, 2024
Summary
This study introduces TCDDU-Net, a novel deep learning model for accurate retinal vessel segmentation. The method achieves high accuracy in segmenting blood vessels in fundus images, aiding disease diagnosis.
Area of Science:
- Medical imaging analysis
- Deep learning for ophthalmology
Background:
- Accurate retinal blood vessel segmentation is vital for diagnosing eye diseases.
- Challenges include small vessel size, complex structures, and low contrast in fundus images.
Purpose of the Study:
- To develop an advanced deep learning model for improved retinal vessel segmentation.
- To address the limitations of existing methods in segmenting complex retinal vasculature.
Main Methods:
- Proposed TCDDU-Net, a dual-path U-Net incorporating transformer and convolutional components.
- Introduced a selective dense connection Swin transformer block for feature fusion and long-distance dependency capture.
- Designed a background decoder using deformable convolution for enhanced segmentation.
Main Results:
- Achieved high segmentation accuracies of 96.98% (DRIVE), 97.40% (STARE), and 97.23% (CHASE).
- Obtained excellent AUC metrics of 98.68% (DRIVE), 98.56% (STARE), and 98.50% (CHASE).
- Demonstrated superior performance over existing methods across multiple datasets using F1 score, specificity, and sensitivity.
Conclusions:
- The TCDDU-Net method significantly enhances retinal vessel segmentation performance.
- The proposed approach offers a robust solution for clinical applications in ophthalmology.
- This advancement contributes to improved diagnostic efficiency and disease management.
