Yu-Wen Luo
Papers
2
Total Citations
14
H-Index
2
About
Yu-Wen Luo is a rising researcher in intelligent robot-assisted surgery and medical image analysis, with a focus on enhancing intra-operative perception for micro-neurosurgical procedures. Their major contributions center on developing deep learning frameworks to overcome critical challenges in surgical scene understanding, particularly motion estimation under occlusion and real-time instrument-tissue segmentation. Luo's work addresses the limitations of traditional optical flow methods by introducing a motion decoupling network that disentangles instrument and tissue motion from occluded intra-operative images, achieving robust tracking in complex surgical environments. Additionally, their high correlative non-local network enables fast, accurate segmentation of surgical tools and soft tissues, directly supporting safer, more efficient robot-assisted interventions. Though early in their career, Luo's papers have already garnered citations, reflecting the immediate relevance of their solutions to pressing clinical needs. Their research bridges computer vision and surgical robotics, offering practical pathways for improving precision in minimally invasive neurosurgery.
Research Focus
Key Achievements
Top Papers
- 1
- 2