3D spine reconstruction of postoperative patients from multi-level manifold ensembles

Automatic Global Level Set Approach for Lumbar Vertebrae CT Image Segmentation

Vertebrae computed tomography (CT) image automatic segmentation is an essential step for Image-guided minimally invasive spine surgery. However, most of state-of-the-art methods still require human intervention due to the inherent limitations of vertebrae CT image, such as topological variation, irregular boundaries (double boundary, weak boundary), and image noise. Therefore, this paper intentionally designed an automatic global level set approach (AGLSA), which is capable of dealing with these issues for lumbar vertebrae CT image segmentation. Unlike the traditional level set methods, we firstly propose an automatically initialized level set function (AILSF) that comprises hybrid morphological filter (HMF) and Gaussian mixture model (GMM) to automatically generate a smooth initial contour which is precisely adjacent to the object boundary. Secondly, a regularized level set formulation is introduced to overcome the weak boundary leaking problem, which utilizes the region correlation of histograms inside and outside the level set contour as a global term. Ultimately, a gradient vector flow (GVF) based edge-stopping function is employed to guarantee a fast convergence rate of the level set evolution and to avoid level set function oversegmentation at the same time. Our proposed approach has been tested on 115 vertebrae CT volumes of various patients. Quantitative comparisons validate that our proposed AGLSA is more accurate in segmenting lumbar vertebrae CT images with irregular boundaries and more robust to various levels of salt-and-pepper noise.

Postoperative 3D spine reconstruction by navigating partitioning manifolds

PURPOSE

The postoperative evaluation of scoliosis patients undergoing corrective treatment is an important task to assess the strategy of the spinal surgery. Using accurate 3D geometric models of the patient's spine is essential to measure longitudinal changes in the patient's anatomy. On the other hand, reconstructing the spine in 3D from postoperative radiographs is a challenging problem due to the presence of instrumentation (metallic rods and screws) occluding vertebrae on the spine.

METHODS

This paper describes the reconstruction problem by searching for the optimal model within a manifold space of articulated spines learned from a training dataset of pathological cases who underwent surgery. The manifold structure is implemented based on a multilevel manifold ensemble to structure the data, incorporating connections between nodes within a single manifold, in addition to connections between different multilevel manifolds, representing subregions with similar characteristics.

RESULTS

The reconstruction pipeline was evaluated on x-ray datasets from both preoperative patients and patients with spinal surgery. By comparing the method to ground-truth models, a 3D reconstruction accuracy of 2.24 ± 0.90 mm was obtained from 30 postoperative scoliotic patients, while handling patients with highly deformed spines.

CONCLUSIONS

This paper illustrates how this manifold model can accurately identify similar spine models by navigating in the low-dimensional space, as well as computing nonlinear charts within local neighborhoods of the embedded space during the testing phase. This technique allows postoperative follow-ups of spinal surgery using personalized 3D spine models and assess surgical strategies for spinal deformities.

Characterizing the differences between the 2D and 3D measurements of spine in adolescent idiopathic scoliosis

PURPOSE

Although adolescent idiopathic scoliosis (AIS) is known to impact the 3D orientation of the spine and pelvis, the impact of the vertebral position relative to the X-ray scanner on the agreement between 2D and 3D measurements of a curve has not been evaluated. The purpose of this study was to investigate the agreement between 2D and 3D measurements of the scoliotic curve as a function of the 3D spinal parameters in AIS.

METHODS

Three independent observers measured the thoracic and lumbar Cobb angles, Kyphosis, and lordosis on the posterior-anterior and lateral X-rays of AIS patients. The 3D reconstructions were created from bi-planar X-rays and the 3D spinal parameters were calculated in both radio and patient planes using SterEOS software. The degree of agreement between the 2D and 3D measurements was tested and its relationship with the curve axial rotation was determined.

RESULTS

2D and 3D measurements of the sagittal plane spinal parameters were significantly different (p < 0.05). The differences between the 2D and 3D measurements were related to the apical vertebrae rotation, the orientation of the plane of maximum curvature, pelvic axial rotation, and the curve magnitude. Differences between the radio plane and patient plane measurements were related to the pelvic axial rotation, Cobb angles, and apical vertebrae rotation, p < 0.05.

CONCLUSION

Clinically and statistically significant differences were observed between the 2D and 3D measurements of the scoliotic spine. The differences between the 2D and 3D techniques were significant in sagittal plane and were related to the spinal curve and pelvic rotation in transverse plane.