Jean‐Marc Peyrat
Papers
9
Total Citations
160
H-Index
7
About
Jean-Marc Peyrat is a leading researcher in computer-assisted surgery, with a primary focus on robot-assisted partial nephrectomy (RAPN) and image-guided robotic interventions. His work centers on developing advanced computer vision and machine learning techniques to enhance surgical perception and decision-making. Peyrat’s major contributions include pioneering methods for simultaneous multi-structure segmentation and 3D nonrigid pose estimation in endoscopic video, enabling real-time contextual awareness for surgeons. He has also developed innovative approaches for automatic segmentation of occluded vasculature using pulsatile motion analysis, and uncertainty-encoded augmented reality systems that improve tumor localization and resection planning. His research on biomechanical kidney models for predicting tumor displacement under external pressure has been particularly impactful, with his most cited paper (49 citations) addressing multi-structure segmentation in image-guided robotic surgery. Peyrat’s work bridges pre-operative imaging and intra-operative guidance, as demonstrated in his studies on multi-modal image fusion for tumor identification. His contributions have been recognized through multiple publications in top surgical robotics venues, and his techniques have direct clinical applications in improving outcomes for minimally invasive kidney cancer surgery.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9