VDSB-GWSyn: Diffusion Schr\"{o}dinger Bridge for Controllable and Anatomically Feasible Guidewire Synthesis in Coronary Angiography

Researchers propose VDSB-GWSyn, a diffusion-based AI framework that synthesizes realistic coronary guidewire images for training computer-assisted surgical systems. The model generates anatomically feasible guidewire samples with precise endpoint localization, improving downstream detection accuracy from 52.63% to 86.27% and reducing localization error by 52%, potentially advancing robot-assisted cardiac interventions.

This research addresses a critical bottleneck in medical AI development: the scarcity of annotated training data for specialized surgical applications. Coronary guidewire localization is essential for percutaneous coronary intervention (PCI) procedures, particularly as hospitals increasingly adopt robot-assisted systems to minimize operator radiation exposure. The VDSB-GWSyn framework solves this through synthetic data generation using a Diffusion Schrödinger Bridge model, which learns geometric constraints from actual vessel anatomy while preserving realistic background details. This approach demonstrates the broader trend of using generative AI to solve domain-specific data scarcity problems in healthcare, where privacy concerns and annotation costs limit available training sets.

The technical innovation lies in the framework's multi-stage design: it first learns guidewire geometry through shape priors, then generates masks constrained by vessel segmentation, and finally synthesizes photorealistic images on real angiography backgrounds. The dramatic performance improvements—nearly 34-point gains in point-to-click accuracy metrics—suggest that synthetic pre-training followed by real fine-tuning effectively transfers learned features to real-world deployment scenarios.

For the medical device and healthcare AI sectors, this work demonstrates a scalable pathway for developing perception systems in data-scarce surgical domains. The methodology's transferability to other interventional device detection tasks creates a template for accelerating AI adoption in specialized medical procedures. As robot-assisted surgery gains market traction, improved perception systems directly enhance clinical reliability and reduce procedural risks, making this research relevant to device manufacturers, hospital systems, and AI researchers focused on medical applications. Success here could prompt similar synthetic data approaches across cardiology, orthopedics, and other procedure-heavy specialties.

• →Synthetic data generation addresses critical annotation scarcity in surgical AI without requiring additional patient imaging
• →Framework improves guidewire localization accuracy to 86.27%, substantially exceeding baseline performance of 52.63%
• →Diffusion Schrödinger Bridge with anatomical constraints ensures generated samples remain clinically realistic and feasible
• →Methodology demonstrates transferability across interventional device perception tasks in healthcare
• →Advancement supports broader adoption of robot-assisted procedures by improving computer vision reliability