Xinran Zhang

Xinran Zhang received the B.S. degree in biomedical engineering from South China University of Technology, Guangzhou, China, in 2013, and the Master’s degree in the Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, in 2016. Her research interests include high quality and accuracy integral videography technologies and its application in 3-D surgical guidance system.

Associated articles

JBHI, Featured Articles
Moving-tolerant Augmented Reality Surgical Navigation System using Autostereoscopic 3D Image Overlay
     Augmented reality (AR) surgical navigation systems based on image overlay have been used in minimally invasive surgery (MIS). However, conventional systems still suffer from a limited viewing zone, a shortage of intuitive three-dimensional (3D) image guidance and can’t be moved... Read more
TBME, Featured Articles
A Cascaded Deep Convolutional Neural Network for Joint Segmentation and Genotype Prediction of Brainstem Gliomas
Brainstem gliomas (BSGs) are a cancerous glioma tumor that occur in the brainstem. Diffuse intrinsic pontine gliomas (DIPG) account for 80% of BSGs in children and 45~50% in adults. Nearly 80% of pediatric DIPGs are induced by reprogramming the histone... Read more
TBME, Featured Articles
High Quality See-Through Surgical Guidance System Using Enhanced 3D Autostereoscopic Augmented Reality
Pre-/intraoperative diagnosis images in minimally invasive surgery can provide necessary guidance for therapy, hand–eye discoordination occurs when guidance information is displayed away from the surgical area. Three-dimensional (3-D) and augmented reality (AR) visualization techniques can provide better judgement and faster... Read more
TBME, Featured Articles
Tissue Structure Updating for In Situ Augmented Reality Navigation using Calibrated Ultrasound and Two-level Surface Warping
In minimally invasive surgery, in situ augmented reality (AR) navigation systems are usually implemented using a glasses-free 3D display to represent the preoperative tissue structure. However, due to changes in intraoperative tissue, the preoperative tissue structure is not able to exactly correspond to reality. To solve this problem, we propose a method to update the tissue structure for in situ AR navigation in such way to reflect changes in intraoperative tissue. Experiments confirm that the novel AR navigation system based on updating the tissue structure will open up a better approach to provide accurate 3D see-through guidance... Read more