Xiaoxuan Zhang
Papers
2
Total Citations
18
H-Index
2
About
Xiaoxuan Zhang is a biomedical engineer whose research sits at the intersection of computer vision, surgical robotics, and intraoperative image guidance. Her work focuses on developing data-driven methods to overcome fundamental limitations in surgical navigation—particularly the challenges of limited capture range, model validity, and real-time performance during spine and neurosurgical procedures. In her highly cited 2020 study, Zhang pioneered the use of deep convolutional neural networks to provide robust initialization for 3D-2D registration of spine surgery instrumentation, directly addressing the failure modes of conventional model-based algorithms. Her 2023 work on real-time 3D video reconstruction for transventricular neurosurgery introduced a SLAM-based endoscopic registration method that compensates for brain deformation—a critical advance for neuroendoscopic approaches to deep-brain targets. With each of these papers garnering 9 citations, Zhang’s contributions are already shaping how surgeons visualize and navigate during minimally invasive procedures. Her work promises to improve the accuracy and safety of image-guided interventions, making her a rising voice in the field of surgical data science.
Research Focus
Key Achievements
Top Papers
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