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Multi-Level Attention Network for Retinal Vessel Segmentation
Insights
A new deep learning model, AACA-MLA-D-UNet, improves retinal vessel segmentation for diagnosing eye and heart diseases. This model enhances accuracy while maintaining low complexity, aiding in early disease detection.
Area of Science:
- Ophthalmology
- Medical Imaging
- Computer Vision
Background:
- Accurate retinal vessel segmentation is crucial for diagnosing cardiovascular and ophthalmologic diseases.
- Challenges include limited annotated data, varying vessel sizes, and complex structures.
Purpose of the Study:
- To propose a novel deep learning model, AACA-MLA-D-UNet, for accurate retinal vessel segmentation.
- To address the challenges of low-level detail utilization and feature integration in U-Net architectures.
Main Methods:
- Developed AACA-MLA-D-UNet based on U-Net architecture.
- Incorporated dropout dense blocks to preserve vessel information and prevent overfitting.
- Integrated an adaptive atrous channel attention module in the contracting path.
- Utilized a multi-level attention module in the expanding path to refine features.
Main Results:
- Validated on DRIVE, STARE, and CHASE_DB1 datasets.
- Achieved superior or comparable performance in retinal vessel segmentation.
- Demonstrated lower model complexity compared to existing methods.
- Showcased effectiveness in challenging cases and strong generalization ability.
Conclusions:
- The AACA-MLA-D-UNet model offers an effective solution for retinal vessel segmentation.
- The model's design enhances feature utilization and robustness.
- It holds potential for improved screening and diagnosis of related diseases.
Abstract:
Automatic vessel segmentation in the fundus images plays an important role in the screening, diagnosis, treatment, and evaluation of various cardiovascular and ophthalmologic diseases. However, due to the limited well-annotated data, varying size of vessels, and intricate vessel structures, retinal vessel segmentation has become a long-standing challenge. In this paper, a novel deep learning model called AACA-MLA-D-UNet is proposed to fully utilize the low-level detailed information and the complementary information encoded in different layers to accurately distinguish the vessels from the background with low model complexity. The architecture of the proposed model is based on U-Net, and the dropout dense block is proposed to preserve maximum vessel information between convolution layers and mitigate the over-fitting problem. The adaptive atrous channel attention module is embedded in the contracting path to sort the importance of each feature channel automatically. After that, the multi-level attention module is proposed to integrate the multi-level features extracted from the expanding path, and use them to refine the features at each individual layer via attention mechanism. The proposed method has been validated on the three publicly available databases, i.e. the DRIVE, STARE, and CHASE _ DB1. The experimental results demonstrate that the proposed method can achieve better or comparable performance on retinal vessel segmentation with lower model complexity. Furthermore, the proposed method can also deal with some challenging cases and has strong generalization ability.
