This work presents a novel tool-free neuronavigation method that can be used with a single RGB commodity camera. Compared with freehand craniotomy placement methods, the proposed system is more intuitive and less error prone. The proposed method also has several advantages over standard neuronavigation platforms. First, it has a much lower cost, since it doesn't require the use of an optical tracking camera or electromagnetic field generator, which are typically the most expensive parts of a neuronavigation system, making it much more accessible. Second, it requires minimal setup, meaning that it can be performed at the bedside and in circumstances where using a standard neuronavigation system is impractical. Our system relies on machine-learning-based hand pose estimation that acts as a proxy for optical tool tracking, enabling a 3D-3D pre-operative to intra-operative registration. Qualitative assessment from clinical users showed that the concept is clinically relevant. Quantitative assessment showed that on average a target registration error (TRE) of 1.3cm can be achieved. Furthermore, the system is framework-agnostic, meaning that future improvements to hand-tracking frameworks would directly translate to a higher accuracy.
Keywords: Augmented Reality; Computer-aided Interventions; Image-guided Neurosurgery; Neuronavigation; Visualization.