Optimized Vessel Segmentation: A Structure-Agnostic Approach With Small Vessel Enhancement and Morphological...

Area of Science:

• Medical Imaging
• Computer Vision
• Biomedical Engineering

Background:

• Accurate blood vessel segmentation is crucial for clinical analysis but faces challenges like sparsity, low contrast, and maintaining topological integrity.
• Existing specialized and general-purpose segmentation models struggle with generalization and preserving vascular connectivity.

Purpose of the Study:

• To introduce OVS-Net, an optimized vessel segmentation framework designed for improved generalization across diverse vascular structures and imaging modalities.
• To address limitations in small vessel segmentation and ensure the preservation of vascular topology and connectivity.

Main Methods:

• Developed OVS-Net featuring a dual-branch architecture for enhanced small vessel segmentation.
• Incorporated a morphology-aware correction module to maintain vascular topology and connectivity.
• Compiled a multi-modality dataset from 17 sources for comprehensive training and benchmarking.

Main Results:

• OVS-Net demonstrated superior segmentation accuracy and generalization capabilities compared to 6 SAM-based and 17 expert models.
• Achieved a significant 34.6% improvement in segmentation connectivity.
• Validated performance across various imaging conditions and vessel types.

Conclusions:

• OVS-Net provides a robust and generalizable solution for blood vessel segmentation.
• The framework's ability to preserve connectivity and handle diverse vascular structures shows significant potential for clinical applications.
• The availability of code and dataset facilitates further research and development.