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MCP-Net: A Simulation-Driven Coronary Artery Perception Framework
1 reports · 1 sources
Sahar Nasirihaghighi and co-authors proposed a simulation-driven framework centered on the multi-task coronary artery perception network MCP-Net for robotic guidewire navigation, integrating coronary vessel segmentation, vessel topology extraction, and roadmap generation. The method generates simulated fluoroscopic images from three-dimensional coronary CT angiography; the images contain vessel structures, simulated guidewires, stenotic lesions of varying severity, and multiple vessel geometries, and come with simulation ground truth. MCP-Net jointly performs vessel segmentation, guidewire tip localization, and stenosis localization from a single fluoroscopic frame. The above content comes from the authors' description in the paper; the report does not provide validation results of this framework on real clinical data or in robotic navigation.
Report timeline
- MCP-Net: A Real-Time Coronary Perception Network and Topological Analysis for Robotic Guidewire Navigation
The authors propose a simulation-driven framework centered on a multi-task coronary perception network, MCP-Net, for robotic guidewire navigation, integrating coronary vessel segmentation, vascular topology extraction, and roadmap generation. The method generates simulated fluoroscopy images and simulation-derived ground truth from three-dimensional coronary CT angiography, including simulated guidewires, stenotic lesions of varying severity, and diverse vascular geometries. MCP-Net jointly performs vessel segmentation, guidewire-tip localization, and stenosis localization from a single fluoroscopic frame.
arXiv cs.RO ·