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.
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.