Xiao-Lan Liao
Papers
4
Total Citations
37
H-Index
4
About
Xiao-Lan Liao is a pioneering researcher at the intersection of surgical robotics and medical image analysis, with a primary focus on advancing robot-assisted orthopedic surgery. Her most impactful work centers on autonomous path planning for pelvic fracture closed reduction, where she has developed innovative algorithms that address critical surgical challenges. Liao's 2022 paper on collision-avoidance path planning for robot-assisted pelvic fracture reduction has garnered 16 citations, establishing a foundation for safer surgical automation. She further advanced the field by integrating muscle force optimization into path planning algorithms, a novel approach that reduces intraoperative resistance and enhances patient safety. Beyond path planning, Liao has contributed to dynamic surgical action recognition and expert movement mapping, enabling more intuitive robot-assisted procedures. Her work in semi-supervised medical image segmentation, guided by bi-directional constrained dual-task consistency, addresses the critical challenge of limited annotated medical data, achieving robust multi-object segmentation for surgical planning. With a growing citation record across these interconnected domains, Liao is recognized for translating complex biomechanical and computational challenges into practical solutions that improve surgical precision and outcomes in pelvic trauma care.
Research Focus
Key Achievements
Top Papers
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