Matin Torabinia
Papers
1
Total Citations
11
H-Index
1
About
Matin Torabinia is a researcher at the forefront of medical imaging and minimally invasive surgery, with a focus on integrating deep learning with interventional guidance systems. His work centers on advancing catheter tracking and navigation technologies, particularly for complex cardiac procedures. Torabinia’s most cited paper, "Deep learning-driven catheter tracking from bi-plane X-ray fluoroscopy of 3D printed heart phantoms" (2021), has garnered 11 citations and exemplifies his innovative approach—combining 3D-printed anatomical models with neural networks to enhance real-time instrument localization. This contribution addresses a critical challenge in robotic surgical systems and fluoroscopic navigation, aiming to improve precision and safety in catheter-based interventions. By bridging computational methods with practical clinical tools, Torabinia’s research supports the expansion of minimally invasive surgery into more intricate operations. His work underscores a commitment to translating AI-driven solutions into tangible improvements for surgical guidance, making him a notable figure in the intersection of biomedical engineering and computer vision.
Research Focus
Key Achievements
Top Papers
- 1