Vol. 23, Issue 6 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. However, conventional systems still suffer from a limited viewing zone, a shortage of intuitive three-dimensional (3D) image guidance and can’t be moved freely. To fuse the 3D overlay image with the patient in situ, it is essential to track the overlay device while it is moving.
A Cascaded Deep Convolutional Neural Network for Joint Segmentation and Genotype Prediction of Brainstem Gliomas
The precise segmentation of Brainstem gliomas (BSGs) tissue is crucial for surgical planning and radiomics. We present a cascaded deep convolutional neural network (CNN) for segmentation and genotype prediction of brainstem gliomas simultaneously. Segmentation task contains two feature-fusion modules: Gaussian-pyramid multiscale input and region-enhancement. Prediction model combines CNN features and support-vector-machine classifier to automatically predict genotypes. Experiments demonstrate that our method achieves good tumor segmentation results and competitive genotype prediction results.
Spatial Position Measurement System for Surgical Navigation Using 3-D Image Marker-Based Tracking Tools With Compact Volume
We proposed a spatial position measurement system using 3-D image marker and tracking tools with both compact volume and high positional measurement accuracy for surgical navigation, especially in limited surgical environment in minimally invasive surgery.
High Quality See-Through Surgical Guidance System Using Enhanced 3D Autostereoscopic Augmented Reality
We propose a high quality real 3D see-through surgical guidance system for precise and minimal invasive surgery. The system provides a novel approach to assist precise and safe clinical microsurgeries with intuitive and reliable diagnosis images.