Geometric-topological deep transfer learning for precise vessel segmentation in 3D medical volumes

Area of Science:

• Medical Imaging
• Computational Anatomy
• Geometric Modeling

Background:

• Current voxel-based methods for vascular modeling struggle with topological accuracy and fragmentation.
• These limitations hinder precise clinical diagnostics and therapeutic planning.

Purpose of the Study:

• Introduce FlowAxis, a novel continuous parameterization paradigm for vascular architectures.
• Address the topological and fragmentation issues inherent in discrete voxel-wise representations.

Main Methods:

• Develop Adaptive Vessel Axes (AVA) using interconnected keypoints for intrinsic topology encapsulation.
• Utilize displacement convexity of the energy functional for guaranteed topological coherence.
• Provide a complete theoretical framework for continuous vessel representation with optimality and convergence guarantees.

Main Results:

• Achieve significant improvements in topological accuracy (clDice) and geometric fidelity (Hausdorff distance) across four 3D vascular segmentation datasets.
• Demonstrate superior topological coherence compared to existing methods.
• Validate performance through comprehensive empirical testing.

Conclusions:

• FlowAxis provides a robust and theoretically sound framework for continuous vessel representation in medical imaging.
• The method enhances accuracy and reliability, offering transformative potential for clinical workflows.
• This work bridges mathematical rigor with practical medical imaging applications.