VDSB-GWSyn: Diffusion Schr\"{o}dinger Bridge for Controllable and Anatomically Feasible Guidewire Synthesis in Coronary Angiography
Quick Take
The VDSB-GWSyn framework utilizes a Diffusion Schrödinger Bridge model to synthesize high-fidelity guidewire samples for coronary angiography, significantly improving endpoint localization accuracy from 16.01 px to 7.71 px and increasing PCK at 3 px from 52.63% to 86.27%. This advancement enhances the deployment of robot-assisted guidewire systems in clinical settings.
Key Points
- VDSB-GWSyn synthesizes guidewire samples under complex anatomical constraints.
- Achieves improved ROI-FID and ROI-KID scores in experimental results.
- Synthetic pre-training with generated data enhances localization performance.
- MPE reduced from 16.01 px to 7.71 px with PCK at 3 px increasing to 86.27%.
- Framework design can be adapted for other interventional device tasks.
Article Content
From source RSS / original summaryarXiv:2606. 00109v1 Announce Type: new 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, high-fidelity guidewire samples under complex anatomical backgrounds. VDSB-GWSyn first uses our shape prior algorithm to learn the basic guidewire geometry. It then generates guidewire masks under constraints imposed by the vessel segmentation masks and outputs the corresponding endpoint coordinates.
Finally, it synthesizes realistic guidewire samples on real CAG images using DSB conditioned with SPADE. Experimental results show that the guidewire samples synthesized by VDSB-GWSyn achieve favorable ROI-FID and ROI-KID, as well as high IPR scores. In addition, incorporating our synthesized data for synthetic pre-training followed by real fine-tuning substantially improves downstream guidewire endpoint localization, reducing MPE from 16. 01~px to 7. 71~px and increasing PCK at 3~px from 52. 63\% to 86.
27\%, leading to more clinically reliable deployment of robot-assisted guidewire delivery systems. Moreover, the core design philosophy of controllable device synthesis with strict background preservation and anatomical feasibility constraints has the potential to transfer to other interventional device perception tasks where annotated data are scarce.
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