Domain generalization for retinal vessel segmentation via Hessian-based vector field.

Area of Science:

• Computer Vision
• Medical Image Analysis
• Machine Learning

Background:

• Deep learning models excel in computer vision and medical imaging but struggle with domain shift due to data variations.
• Lack of imaging standardization in medical data distribution hinders the deployment of deep learning models.
• Domain generalization (DG) methods are crucial for improving model robustness across diverse datasets.

Purpose of the Study:

• To develop a robust domain generalization method for medical image analysis.
• To address the challenge of domain shift in deep learning models for medical imaging.
• To improve the generalizability of models across different imaging modalities and data distributions.

Main Methods:

• Introduced a Hessian-based vector field to model invariant tubular vessel structures.
• Utilized the vector field as an embedding feature for a vision transformer with self-attention.
• Designed paralleled transformer blocks to emphasize local features at multiple scales.
• Developed a novel data augmentation technique preserving vessel structure while altering image style.

Main Results:

• The proposed method demonstrated superior generalizability on public datasets of varying modalities.
• The Hessian-based vector field effectively captured invariant vessel features across distributions.
• The vision transformer architecture, enhanced by the vector field, showed improved performance.

Conclusions:

• The developed domain generalization approach significantly enhances model robustness in medical imaging.
• The Hessian-based vector field is a promising invariant feature for cross-domain medical image analysis.
• The method offers a viable solution for deploying deep learning models in diverse clinical settings.