Joint high-resolution feature learning and vessel-shape aware convolutions for efficient vessel segmentation.

Area of Science:

• Ophthalmology
• Medical Imaging
• Computer Vision

Background:

• Clear retinal vessel imagery is crucial for diagnosing and evaluating diseases.
• Existing methods struggle to capture the complex topology and fine details of retinal vasculature.

Purpose of the Study:

• To develop a topology- and shape-aware model for high-resolution retinal vessel analysis.
• To improve the accuracy of capturing intricate vessel structures and disease-related information.

Main Methods:

• Proposed a Multi-branch Vessel-shaped Convolution Network (MVCN) for adaptive learning of high-resolution representations.
• Introduced a Multiple High-resolution Ensemble Module (MHEM) to enhance scale-invariant hierarchical topology.
• Developed a novel vessel-shaped convolution and utilized epistemic uncertainty for dynamical sub-label generation.

Main Results:

• Achieved state-of-the-art AUC values (up to 98.83%) and accuracy (up to 97.09%) across multiple datasets (DRIVE, CHASE_DB1, STARE, HRF).
• Demonstrated significantly improved skeletal similarity metrics (correctness, completeness, quality), with evaluations doubling compared to previous methods.

Conclusions:

• The MVCN model effectively captures high-quality topology and shape information from retinal vessel imagery.
• The proposed method offers superior performance in retinal vessel analysis for disease diagnosis and evaluation.