The spine surgery simulator S3 has proven its technical feasibility and clinical relevance to assist in the preoperative planning of instrumentation strategies for the correction of scoliotic deformities.

Abstract.A robust method to segment intervertebral disks and spinal canal in magnetic resonance images is required as part of a precise 3D reconstruction for computer assistance during diskectomy procedure with minimally invasive surgery approach. In this paper, an unsupervised segmentation technique for intervertebral disks and spinal canal from MRI data is presented. The proposed scheme uses a watershed transform and morphological operations to locate regions containing structures of interest. Results show that the method is robust enough to cope with variability of shapes and topologies characterizing MRI images of scoliotic patients.

This paper presents a unified framework for automatic segmentation of intervertebral disks of scoliotic spines from different types of magnetic resonance (MR) image sequences. The method exploits a combination of statistical and spectral texture features to discriminate closed regions representing intervertebral disks from background in MR images of the spine. Specific texture features are evaluated for three types of MR sequences acquired in the sagittal plane: 2-D spin echo, 3-D multiecho data image combination, and 3-D fast imaging with steady state precession. A total of 22 texture features (18 statistical and 4 spectral) are extracted from every closed region obtained from an automatic segmentation procedure based on the watershed approach. The feature selection step based on principal component analysis and clustering process permit to decide among all the extracted features which ones resulted in the highest rate of good classification. The proposed method is validated using a supervised k-nearest-neighbor classifier on 505 MR images coming from three different scoliotic patients and three different MR acquisition protocols. Results suggest that the selected texture features and classification can contribute to solve the problem of oversegmentation inherent to existing automatic segmentation methods by successfully discriminating intervertebral disks from the background on MRI of scoliotic spines.

Background: For some scoliotic patients the spinal instrumentation is inevitable. Among these patients, those with stiff curvature will need thoracoscopic disk resection. The removal of the intervertebral disk with only thoracoscopic images is a tedious and challenging task for the surgeon. With computer aided surgery and 3D visualisation of the interverterbral disk during surgery, surgeons will have access to additional information such as the remaining disk tissue or the distance of surgical tools from critical anatomical structures like the aorta or spinal canal. We hypothesized that automatically extracting 3D information of the intervertebral disk from MR images would aid the surgeons to evaluate the remaining disk and would add a security factor to the patient during thoracoscopic disk resection. Methods: This paper presents a quantitative evaluation of an automatic segmentation method for 3D reconstruction of intervertebral scoliotic disks from MR images. The automatic segmentation method is based on the watershed technique and morphological operators. The 3D Dice Similarity Coefficient (DSC) is the main statistical metric used to validate the automatically detected preoperative disk volumes. The automatic detections of intervertebral disks of real clinical MR images are compared to manual segmentation done by clinicians. Results: Results show that depending on the type of MR acquisition sequence, the 3D DSC can be as high as 0.79 (±0.04). These 3D results are also supported by a 2D quantitative evaluation as well as by robustness and variability evaluations. The mean discrepancy (in 2D) between the manual and automatic segmentations for regions around the spinal canal is of 1.8 (±0.8) mm. The robustness study shows that among the five factors evaluated, only the type of MRI acquisition sequence can affect the segmentation results. Finally, the variability of the automatic segmentation method is lower than the variability associated with manual segmentation performed by different physicians. Conclusions: This comprehensive evaluation of the automatic segmentation and 3D reconstruction of intervertebral disks shows that the proposed technique used with specific MRI acquisition protocol can detect intervertebral disk of scoliotic patient. The newly developed technique is promising for clinical context and can eventually help surgeons during thoracoscopic intervertebral disk resection.