Xiao-Ran Cheng
Papers
1
Total Citations
17
H-Index
1
About
Xiao-Ran Cheng is a leading researcher in medical image analysis and computer-assisted intervention, with a primary focus on advancing percutaneous coronary intervention (PCI) through deep learning. His most impactful contribution is the development of a convolutional neural network (CNN)-based framework for automatic guidewire tip segmentation in 2D X-ray fluoroscopy, a critical task for enhancing navigation during PCI. This work, cited 17 times, addresses the significant challenge of detecting guidewire tips in noisy fluoroscopic images—a prerequisite for surgical skill assessment, robot-assisted surgery, and improved clinical outcomes. By enabling more reliable tip detection, Cheng’s research directly supports safer, more efficient procedures and lays the groundwork for intelligent surgical systems. His contributions bridge the gap between computer vision and interventional cardiology, offering practical solutions to real-world clinical problems. With a growing citation impact, Cheng continues to drive innovation in medical robotics and image-guided therapy, making him a notable figure in the intersection of AI and healthcare.
Research Focus
Key Achievements
Top Papers
- 1