Modeling of the bony pelvis from MRI using a multi-atlas AE-SDM for registration and tracking in image-guided robotic prostatectomy
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 1.240.27mm, 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 0.13mm 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.
statistic deformation model hierarchical registration multi-atlas segmentation multi-modality registration augmented reality dense tracking and mapping