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TDCAU-Net: retinal vessel segmentation using transformer dilated convolutional attention-based U-Net method
Chunyang Li1, Zhigang Li1, Weikang Liu1
1School of Electronics and Information Engineering, University of Science and Technology Liaoning, Anshan, People's Republic of China.
Insights
A new Transformer dilated convolution attention U-Net (TDCAU-Net) improves retinal vessel segmentation for detecting chronic conditions. This novel method enhances accuracy in segmenting fine branching and dense vessels, outperforming existing techniques.
Area of Science:
- Medical imaging analysis
- Computer vision in healthcare
- Ophthalmology diagnostics
Background:
- Retinal vessel segmentation is crucial for diagnosing conditions like diabetic retinopathy and glaucoma.
- The U-Net model shows promise but struggles with segmenting fine and dense retinal vessels.
- Accurate segmentation aids in early disease detection and management.
Purpose of the Study:
- To introduce a novel TDCAU-Net model for enhanced retinal vessel segmentation.
- To improve the precision of segmenting fine branching and dense vessels.
- To evaluate the proposed model against state-of-the-art methods.
Main Methods:
- Developed a TDCAU-Net model based on the U-Net architecture with Transformer-based dilated convolution attention.
- Implemented a five-step preprocessing and image segmentation pipeline.
- Trained and tested the model on the DRIVE and CHASEDB1 eye fundus image databases.
Main Results:
- The TDCAU-Net model achieved high sensitivity, specificity, accuracy, and AUC on both datasets.
- Achieved 0.8187 sensitivity, 0.9756 specificity, 0.9556 accuracy, and 0.9795 AUC on the DRIVE database.
- Achieved 0.8243 sensitivity, 0.9836 specificity, 0.9738 accuracy, and 0.9878 AUC on the CHASEDB1 database.
Conclusions:
- The TDCAU-Net model significantly outperforms U-Net and other mainstream methods in retinal vessel segmentation.
- The proposed approach demonstrates superior performance in segmenting fine branching and dense vessels.
- TDCAU-Net offers a promising advancement for automated diagnosis of retinal diseases.
Abstract:
Retinal vessel segmentation plays a vital role in the medical field, facilitating the identification of numerous chronic conditions based on retinal vessel images. These conditions include diabetic retinopathy, hypertensive retinopathy, glaucoma, and others. Although the U-Net model has shown promising results in retinal vessel segmentation, it tends to struggle with fine branching and dense vessel segmentation. To further enhance the precision of retinal vessel segmentation, we propose a novel approach called transformer dilated convolution attention U-Net (TDCAU-Net), which builds upon the U-Net architecture with improved Transformer-based dilated convolution attention mechanisms. The proposed model retains the three-layer architecture of the U-Net network. The Transformer component enables the learning of contextual information for each pixel in the image, while the dilated convolution attention prevents information loss. The algorithm efficiently addresses several challenges to optimize blood vessel detection. The process starts with five-step preprocessing of the images, followed by chunking them into segments. Subsequently, the retinal images are fed into the modified U-Net network introduced in this paper for segmentation. The study employs eye fundus images from the DRIVE and CHASEDB1 databases for both training and testing purposes. Evaluation metrics are utilized to compare the algorithm's results with state-of-the-art methods. The experimental analysis on both databases demonstrates that the algorithm achieves high values of sensitivity, specificity, accuracy, and AUC. Specifically, for the first database, the achieved values are 0.8187, 0.9756, 0.9556, and 0.9795, respectively. For the second database, the corresponding values are 0.8243, 0.9836, 0.9738, and 0.9878, respectively. These results demonstrate that the proposed approach outperforms state-of-the-art methods, achieving higher performance on both datasets. The TDCAU-Net model presented in this study exhibits substantial capabilities in accurately segmenting fine branching and dense vessels. The segmentation performance of the network surpasses that of the U-Net algorithm and several mainstream methods.
