Improved Surface-Based Registration of CT and Intraoperative 3D Ultrasound of Bones.
Zian FantiFabian TorresEric Hazan-LasriAlfonso Gastelum-StrozziLeopoldo Ruiz-HuertaAlberto Caballero-RuizF Arámbula CosíoPublished in: Journal of healthcare engineering (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).
Keyphrases
- magnetic resonance imaging
- patients undergoing
- deep learning
- computed tomography
- image quality
- ultrasound guided
- contrast enhanced
- convolutional neural network
- bone mineral density
- contrast enhanced ultrasound
- minimally invasive
- high resolution
- magnetic resonance
- machine learning
- positron emission tomography
- photodynamic therapy
- bone regeneration
- coronary artery disease
- body composition