VDSB-GWSyn: Diffusion Schr\"{o}dinger Bridge for Controllable and Anatomically Feasible Guidewire Synthesis in Coronary Angiography
arXiv:2606.00109v1 Announce Type: cross Abstract: Coronary guidewire endpoint localization is a fundamental capability for computer-assisted PCI, and its importance increases as robot-assisted PCI is progressively adopted to reduce operator radiation exposure. However, the scarcity of annotated CAG images with guidewires and the limited adaptability of existing guidewire synthesis models remain key bottlenecks for guidewire endpoint localization. To address this issue, we propose VDSB-GWSyn, a Diffusion Schr\"{o}dinger Bridge (DSB) model-based framework, enabling synthesis of controllable, hig
The increasing adoption of robot-assisted PCI highlights a critical bottleneck in robust AI-driven guidewire localization, pushing the need for advanced synthetic data generation techniques.
Improving the synthesis of anatomically feasible and controllable guidewire images addresses data scarcity, accelerating the development of safer and more effective AI for surgical robotics in cardiology.
The ability to generate high-quality synthetic data for guidewire localization will reduce reliance on scarce, manually annotated clinical images, enabling more rapid iteration and deployment of AI-assisted PCI systems.
- · Medical robotics companies
- · Cardiovascular surgery departments
- · AI healthcare developers
- · Manual annotation services for medical imaging
- · Companies with less sophisticated synthetic data capabilities
Improved AI models for guidewire localization lead to enhanced precision and reduced complications in robot-assisted PCI.
The precedent set by VDSB-GWSyn could inspire similar generative AI applications for data augmentation across other medical imaging and surgical domains.
Widespread adoption of such AI could democratize access to advanced surgical techniques by lowering the training barrier and improving outcomes regardless of operator experience.
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Read at arXiv cs.LG