Ping-Lin Chang
Papers
9
Total Citations
376
H-Index
8
About
Ping-Lin Chang is a leading researcher in computer-assisted interventions, with a focus on surgical instrument tracking, pose estimation, and robotic surgery. His work bridges computer vision and medical robotics to enhance the precision and safety of minimally invasive procedures. Chang’s most impactful contribution is his 2018 paper on articulated multi-instrument 2-D pose estimation using fully convolutional networks (136 citations), which tackled the critical challenge of detecting and tracking surgical instruments in video—enabling real-time articulation detection for computer-assisted interventions. He also pioneered robust catheter and guidewire tracking using B-spline tube models and pixel-wise posteriors (45 citations), advancing robotic catheter navigation in endovascular surgery. His earlier work on real-time dense stereo reconstruction for image-guided robotic surgery (49 citations) and multi-class labeling with optical flow for instrument pose estimation (65 citations) laid foundational methods for 3D visualization in the operating room. Chang has further contributed to objective skills assessment in endovascular surgery, with a survey paper (11 citations) and catheter manipulation analysis for transcatheter aortic valve implantation (40 citations). His research has direct clinical impact, including a comparative study of manual versus robot-assisted cannulation in aneurysm repair (13 citations). Chang’s work continues to shape the future of intelligent, vision-guided surgical robotics.
Research Focus
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