3D medical model registration using scale-invariant coherent point drift algorithm for AR

Registering preoperative 3D medical models to the corresponding regions of the individual in a reality scene is a critical foundation for augmented reality-based (AR-based) surgical navigation systems. A challenge is finding an appropriate spatial mapping function from the medical coordinate system to the AR coordinate system. Our work focuses on registering 3D medical models to the intracranial structures using RGBD point clouds. The mapping function is calculated using local facial features and a scale-invariant coherent point drift (SI-CPD) algorithm that eliminates the scaling parameter. The local facial features significantly reduce mismatched features between the 3D medical models and the RGBD point clouds of the scene, while the proposed SI-CPD algorithm restricts the registration process to translation and rotation operations only. Results demonstrate that our method achieves a target registration error (TRE) of 1.2498 ± 0.0829 mm on private medical datasets and superior registration accuracy on the public Stanford Bunny dataset. Compared to ICP-type methods, the SI-CPD algorithm demonstrates enhanced robustness in handling noise and outliers. Our work introduces a novel methodology to automatically register 3D medical models to the head with high accuracy.