TY - JOUR
T1 - Modeling of the bony pelvis from MRI using a multi-atlas AE-SDM for registration and tracking in image-guided robotic prostatectomy
AU - Gao, Qinquan
AU - Chang, Ping Lin
AU - Rueckert, Daniel
AU - Ali, S. Mohammed
AU - Cohen, Daniel
AU - Pratt, Philip
AU - Mayer, Erik
AU - Yang, Guang Zhong
AU - Darzi, Ara
AU - Edwards, Philip Eddie
N1 - Funding Information:
This research was funded by Cancer Research UK under project A8087/C24250 . The Pelican Foundation also funded parts of this research under the Pelvic Anatomy Model (PAM). The authors are grateful for support from the NIHR Biomedical Research Centre funding scheme. We are also grateful to the radiology and theatre staff at the Imperial College Healthcare NHS Trust for their help and cooperation throughout this project. The work has ethical approval from the London-Dulwich research ethics committee.
PY - 2013/3
Y1 - 2013/3
N2 - A fundamental challenge in the development of image-guided surgical systems is alignment of the preoperative model to the operative view of the patient. This is achieved by finding corresponding structures in the preoperative scans and on the live surgical scene. In robot-assisted laparoscopic prostatectomy (RALP), the most readily visible structure is the bone of the pelvic rim. Magnetic resonance imaging (MRI) is the modality of choice for prostate cancer detection and staging, but extraction of bone from MRI is difficult and very time consuming to achieve manually. We present a robust and fully automated multi-atlas pipeline for bony pelvis segmentation from MRI, using a MRI appearance embedding statistical deformation model (AE-SDM). The statistical deformation model is built using the node positions of deformations obtained from hierarchical registrations of full pelvis CT images. For datasets with corresponding CT and MRI images, we can transform the MRI into CT SDM space. MRI appearance can then be used to improve the combined MRI/CT atlas to MRI registration using SDM constraints. We can use this model to segment the bony pelvis in a new MRI image where there is no CT available. A multi-atlas segmentation algorithm is introduced which incorporates MRI AE-SDMs guidance. We evaluated the method on 19 subjects with corresponding MRI and manually segmented CT datasets by performing a leave-one-out study. Several metrics are used to quantify the overlap between the automatic and manual segmentations. Compared to the manual gold standard segmentations, our robust segmentation method produced an average surface distance 1.24±0.27 mm, which outperforms state-of-the-art algorithms for MRI bony pelvis segmentation. We also show that the resulting surface can be tracked in the endoscopic view in near real time using dense visual tracking methods. Results are presented on a simulation and a real clinical RALP case. Tracking is accurate to 0.13. mm over 700 frames compared to a manually segmented surface. Our method provides a realistic and robust framework for intraoperative alignment of a bony pelvis model from diagnostic quality MRI images to the endoscopic view.
AB - A fundamental challenge in the development of image-guided surgical systems is alignment of the preoperative model to the operative view of the patient. This is achieved by finding corresponding structures in the preoperative scans and on the live surgical scene. In robot-assisted laparoscopic prostatectomy (RALP), the most readily visible structure is the bone of the pelvic rim. Magnetic resonance imaging (MRI) is the modality of choice for prostate cancer detection and staging, but extraction of bone from MRI is difficult and very time consuming to achieve manually. We present a robust and fully automated multi-atlas pipeline for bony pelvis segmentation from MRI, using a MRI appearance embedding statistical deformation model (AE-SDM). The statistical deformation model is built using the node positions of deformations obtained from hierarchical registrations of full pelvis CT images. For datasets with corresponding CT and MRI images, we can transform the MRI into CT SDM space. MRI appearance can then be used to improve the combined MRI/CT atlas to MRI registration using SDM constraints. We can use this model to segment the bony pelvis in a new MRI image where there is no CT available. A multi-atlas segmentation algorithm is introduced which incorporates MRI AE-SDMs guidance. We evaluated the method on 19 subjects with corresponding MRI and manually segmented CT datasets by performing a leave-one-out study. Several metrics are used to quantify the overlap between the automatic and manual segmentations. Compared to the manual gold standard segmentations, our robust segmentation method produced an average surface distance 1.24±0.27 mm, which outperforms state-of-the-art algorithms for MRI bony pelvis segmentation. We also show that the resulting surface can be tracked in the endoscopic view in near real time using dense visual tracking methods. Results are presented on a simulation and a real clinical RALP case. Tracking is accurate to 0.13. mm over 700 frames compared to a manually segmented surface. Our method provides a realistic and robust framework for intraoperative alignment of a bony pelvis model from diagnostic quality MRI images to the endoscopic view.
KW - Augmented reality
KW - Dense tracking and mapping
KW - Hierarchical registration
KW - Multi-atlas segmentation
KW - Multi-modality registration
KW - Statistic deformation model
UR - http://www.scopus.com/inward/record.url?scp=84876975137&partnerID=8YFLogxK
U2 - 10.1016/j.compmedimag.2013.01.001
DO - 10.1016/j.compmedimag.2013.01.001
M3 - Article
C2 - 23428829
AN - SCOPUS:84876975137
SN - 0895-6111
VL - 37
SP - 183
EP - 194
JO - Computerized Medical Imaging and Graphics
JF - Computerized Medical Imaging and Graphics
IS - 2
ER -
