基于超限学习机2.5D-3D配准的脊椎手术导航

2.5D-3D Registration Based on Extreme Learning Machine and Its Application in Spinal Surgery Navigation

基于视觉的自动导航是自动手术系统和手术机器人的关键组成部分。手术前基于CT扫描重建病人组织器官的三维模型,术中采集对应器官的二维影像,使其与三维重建模型进行实时匹配和对准,即实现虚-实配准,即可实现计算机视觉辅助的手术导航。面向脊椎这一刚性组织,研究观察影像与三维重建模型的自动刚性配准。为实现快速、鲁棒的特征提取和匹配,提出使用超限学习机构建的深度学习模型,基于大量训练数据学习2.5D深度图像和3D模型关键点的特征提取,并进行三维配准。人体脊椎数据库的配准实验和猪脊椎模拟手术导航的结果表明,上述方法具有训练时间短、匹配精度高、运行速度快等特点,非常适合于脊椎手术导航。

Vision-based automatic surgery navigation is a key component of autonomous surgery systems or surgery robots. The surgeon performs CT scanning and 3D reconstruction for the pathological tissue of the patient before sur- gery. During surgery, 2D images of the corresponding tissue are captured, which are matched and aligned against the reconstructed tissue in real-time. Such virtual-real alignment is a critical step for visual surgery navigation. This pa- per studies the virtual-real alignment problem for the rigid tissue of human spines and geometry-based alignment a- gainst the reconstructed 3D model. The Extreme Learning Machine （ELM） based deep networks is used for 3D criti- cal point extraction and feature computation, based on a large amount of training data. We demonstrate the efficiency and effectiveness of our method in registration experiment of human spine database and automatic navigation on simu- lated pig spine surgery.