Mixed Reality and Deep Learning for External Ventricular Drainage Placement: A Fast and Automatic Workflow for Emergency Treatments

Abstract

The treatment of hydrocephalus is based on anatomical landmarks to guide the insertion of an External Ventricular Drain (EVD). This procedure can benefit from the adoption of Mixed Reality (MR) technology. In this study, we assess the feasibility of a fully automatic MR and deep learning-based workflow to support emergency EVD placement, for which CT images are available and a fast and automatic workflow is needed. The proposed study provides a tool to automatically i) segment the skull, face skin, ventricles and Foramen of Monro from CT scans; ii) import the segmented model in the MR application; iii) register holograms on the patient’s head via a marker-less approach. An ad-hoc evaluation approach including 3D-printed anatomical structures was developed to quantitatively assess the accuracy and usability of the registration workflow.