Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Ciudad Universitaria, Circuito Escolar S/N, 04510 CDMX, Mexico.
Instituto de Ciencias Aplicadas y Tecnología, Universidad Nacional Autónoma de México, Ciudad Universitaria, Circuito Exterior S/N, 04510 CDMX, Mexico.
J Healthc Eng. 2018 Jun 3;2018:2365178. doi: 10.1155/2018/2365178. eCollection 2018.
The intraoperative registration of preoperative CT volumes is a key process of most computer-assisted orthopedic surgery (CAOS) systems. In this work, is reported a new method for automatic registration of long bones, based on the segmentation of the bone cortical in intraoperative 3D ultrasound images. A bone classifier was developed based on features, obtained from the principal component analysis of the Hessian matrix, of every voxel in an intraoperative ultrasound volume. 3D freehand ultrasound was used for the acquisition of the intraoperative ultrasound volumes. Corresponding bone surface segmentations in ultrasound and preoperative CT imaging were used for the intraoperative registration. Validation on a phantom of the tibia produced encouraging results, with a maximum mean segmentation error of 0.34mm (SD=0.26mm) and a registration accuracy error of 0.64mm (SD=0.49mm).
术中 CT 容积的配准是大多数计算机辅助骨科手术 (CAOS) 系统的关键过程。在这项工作中,报告了一种新的基于术中 3D 超声图像骨皮质分割的长骨自动配准方法。基于术中超声体积中每个体素的主成分分析得到的特征,开发了一种骨分类器。3D 自由手超声用于获取术中超声体积。在超声和术前 CT 成像中使用相应的骨表面分割来进行术中配准。对胫骨的仿体进行验证,得到了令人鼓舞的结果,最大平均分割误差为 0.34mm(标准差=0.26mm),配准精度误差为 0.64mm(标准差=0.49mm)。