Tomaž Vrtovec

A review of methods for quantitative evaluation of spinal curvature

The aim of this paper is to provide a complete overview of the existing methods for quantitative evaluation of spinal curvature from medical images, and to summarize the relevant publications, which may not only assist in the introduction of other researchers to the field, but also be a valuable resource for studying the existing methods or developing new methods and evaluation strategies. Key evaluation issues and future considerations, supported by the results of the overview, are also discussed.

A review of methods for evaluating the quantitative parameters of sagittal pelvic alignment

BACKGROUND CONTEXT The sagittal alignment of the pelvis represents the basic mechanism for maintaining postural equilibrium, and a number of methods were developed to assess normal and pathologic pelvic alignments from two-dimensional sagittal radiographs in terms of positional and anatomic parameters. PURPOSE To provide a complete overview of the existing methods for quantitative evaluation of sagittal pelvic alignment and summarize the relevant publications. STUDY DESIGN Review article. METHODS An Internet search for terms related to sagittal pelvic alignment was performed to obtain relevant publications, which were further supplemented by selected publications found in their lists of references. By summarizing the obtained publications, the positional and anatomic parameters of sagittal pelvic alignment were described, and their values and relationships to other parameters and features were reported. RESULTS Positional pelvic parameters relate to the position and orientation of the observed subject and are represented by the sacral slope, pelvic tilt, pelvic overhang, sacral inclination, sacrofemoral angle, sacrofemoral distance, pelvic femoral angle, pelvic angle, and sacropelvic translation. Anatomic pelvic parameters relate to the anatomy of the observed subject and are represented by the pelvisacral angle (PSA), pelvic incidence (PI), pelvic thickness (PTH), sacropelvic angle (PRS1), pelvic radius (PR), femorosacral posterior angle (FSPA), sacral table angle (STA), and sacral anatomic orientation (SAO). The review was mainly focused on the evaluation of anatomic pelvic parameters, as they can be compared among subjects and therefore among different studies. However, ambiguous results were yielded for normal and pathologic subjects, as the reported values show a relatively high variability in terms of standard deviation for every anatomic parameter, which amounts to around 10 mm for PTH and PR; 10° for PSA, PI, and SAO; 9° for PRS1 and FSPA; and 5° for STA in the case of normal subjects and is usually even higher in the case of pathologic subjects. Among anatomic pelvic parameters, PI was the most studied and therefore represents a key parameter in the complex framework of sagittal spinal alignment and related deformities. From the reviewed studies, the regression lines for PI and the corresponding age of the subjects indicate that PI tends to increase with age for normal (PI = +0.17 × age+46.40) and scoliotic (PI = +0.20 × age+50.52) subjects and decrease with age for subjects with spondylolisis or spondylolisthesis (PI = -0.26 × age+75.69). CONCLUSIONS Normative values for anatomic parameters of sagittal pelvic alignment do not exist because the variability of the measured values is relatively high even for normal subjects but can be predictive for spinal alignment and specific spinopelvic pathologies.

Parametric modelling and segmentation of vertebral bodies in 3D CT and MR spine images

Accurate and objective evaluation of vertebral deformations is of significant importance in clinical diagnostics and therapy of pathological conditions affecting the spine. Although modern clinical practice is focused on three-dimensional (3D) computed tomography (CT) and magnetic resonance (MR) imaging techniques, the established methods for evaluation of vertebral deformations are limited to measuring deformations in two-dimensional (2D) x-ray images. In this paper, we propose a method for quantitative description of vertebral body deformations by efficient modelling and segmentation of vertebral bodies in 3D. The deformations are evaluated from the parameters of a 3D superquadric model, which is initialized as an elliptical cylinder and then gradually deformed by introducing transformations that yield a more detailed representation of the vertebral body shape. After modelling the vertebral body shape with 25 clinically meaningful parameters and the vertebral body pose with six rigid body parameters, the 3D model is aligned to the observed vertebral body in the 3D image. The performance of the method was evaluated on 75 vertebrae from CT and 75 vertebrae from T(2)-weighted MR spine images, extracted from the thoracolumbar part of normal and pathological spines. The results show that the proposed method can be used for 3D segmentation of vertebral bodies in CT and MR images, as the proposed 3D model is able to describe both normal and pathological vertebral body deformations. The method may therefore be used for initialization of whole vertebra segmentation or for quantitative measurement of vertebral body deformations.

Automated detection of spinal centrelines, vertebral bodies and intervertebral discs in CT and MR images of lumbar spine

We propose a completely automated algorithm for the detection of the spinal centreline and the centres of vertebral bodies and intervertebral discs in images acquired by computed tomography (CT) and magnetic resonance (MR) imaging. The developed methods are based on the analysis of the geometry of spinal structures and the characteristics of CT and MR images and were evaluated on 29 CT and 13 MR images of lumbar spine. The overall mean distance between the obtained and the ground truth spinal centrelines and centres of vertebral bodies and intervertebral discs were 1.8 +/- 1.1 mm and 2.8 +/- 1.9 mm, respectively, and no considerable differences were detected among the results for CT, T(1)-weighted MR and T(2)-weighted MR images. The knowledge of the location of the spinal centreline and the centres of vertebral bodies and intervertebral discs is valuable for the analysis of the spine. The proposed method may therefore be used to initialize the techniques for labelling and segmentation of vertebrae.

Automated curved planar reformation of 3D spine images.

Traditional techniques for visualizing anatomical structures are based on planar cross-sections from volume images, such as images obtained by computed tomography (CT) or magnetic resonance imaging (MRI). However, planar cross-sections taken in the coordinate system of the 3D image often do not provide sufficient or qualitative enough diagnostic information, because planar cross-sections cannot follow curved anatomical structures (e.g. arteries, colon, spine, etc). Therefore, not all of the important details can be shown simultaneously in any planar cross-section. To overcome this problem, reformatted images in the coordinate system of the inspected structure must be created. This operation is usually referred to as curved planar reformation (CPR). In this paper we propose an automated method for CPR of 3D spine images, which is based on the image transformation from the standard image-based to a novel spine-based coordinate system. The axes of the proposed spine-based coordinate system are determined on the curve that represents the vertebral column, and the rotation of the vertebrae around the spine curve, both of which are described by polynomial models. The optimal polynomial parameters are obtained in an image analysis based optimization framework. The proposed method was qualitatively and quantitatively evaluated on five CT spine images. The method performed well on both normal and pathological cases and was consistent with manually obtained ground truth data. The proposed spine-based CPR benefits from reduced structural complexity in favour of improved feature perception of the spine. The reformatted images are diagnostically valuable and enable easier navigation, manipulation and orientation in 3D space. Moreover, reformatted images may prove useful for segmentation and other image analysis tasks.

A Framework for Automated Spine and Vertebrae Interpolation-Based Detection and Model-Based Segmentation

Automated and semi-automated detection and segmentation of spinal and vertebral structures from computed tomography (CT) images is a challenging task due to a relatively high degree of anatomical complexity, presence of unclear boundaries and articulation of vertebrae with each other, as well as due to insufficient image spatial resolution, partial volume effects, presence of image artifacts, intensity variations and low signal-to-noise ratio. In this paper, we describe a novel framework for automated spine and vertebrae detection and segmentation from 3-D CT images. A novel optimization technique based on interpolation theory is applied to detect the location of the whole spine in the 3-D image and, using the obtained location of the whole spine, to further detect the location of individual vertebrae within the spinal column. The obtained vertebra detection results represent a robust and accurate initialization for the subsequent segmentation of individual vertebrae, which is performed by an improved shape-constrained deformable model approach. The framework was evaluated on two publicly available CT spine image databases of 50 lumbar and 170 thoracolumbar vertebrae. Quantitative comparison against corresponding reference vertebra segmentations yielded an overall mean centroid-to-centroid distance of 1.1 mm and Dice coefficient of 83.6% for vertebra detection, and an overall mean symmetric surface distance of 0.3 mm and Dice coefficient of 94.6% for vertebra segmentation. The results indicate that by applying the proposed automated detection and segmentation framework, vertebrae can be successfully detected and accurately segmented in 3-D from CT spine images.

A review of methods for quantitative evaluation of axial vertebral rotation

Quantitative evaluation of axial vertebral rotation is essential for the determination of reference values in normal and pathological conditions and for understanding the mechanisms of the progression of spinal deformities. However, routine quantitative evaluation of axial vertebral rotation is difficult and error-prone due to the limitations of the observer, characteristics of the observed vertebral anatomy and specific imaging properties. The scope of this paper is to review the existing methods for quantitative evaluation of axial vertebral rotation from medical images along with all relevant publications, which may provide a valuable resource for studying the existing methods or developing new methods and evaluation strategies. The reviewed methods are divided into the methods for evaluation of axial vertebral rotation in 2D images and the methods for evaluation of axial vertebral rotation in 3D images. Key evaluation issues and future considerations, supported by the results of the overview, are also discussed.

A multi-center milestone study of clinical vertebral CT segmentation☆

A multiple center milestone study of clinical vertebra segmentation is presented in this paper. Vertebra segmentation is a fundamental step for spinal image analysis and intervention. The first half of the study was conducted in the spine segmentation challenge in 2014 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshop on Computational Spine Imaging (CSI 2014). The objective was to evaluate the performance of several state-of-the-art vertebra segmentation algorithms on computed tomography (CT) scans using ten training and five testing dataset, all healthy cases; the second half of the study was conducted after the challenge, where additional 5 abnormal cases are used for testing to evaluate the performance under abnormal cases. Dice coefficients and absolute surface distances were used as evaluation metrics. Segmentation of each vertebra as a single geometric unit, as well as separate segmentation of vertebra substructures, was evaluated. Five teams participated in the comparative study. The top performers in the study achieved Dice coefficient of 0.93 in the upper thoracic, 0.95 in the lower thoracic and 0.96 in the lumbar spine for healthy cases, and 0.88 in the upper thoracic, 0.89 in the lower thoracic and 0.92 in the lumbar spine for osteoporotic and fractured cases. The strengths and weaknesses of each method as well as future suggestion for improvement are discussed. This is the first multi-center comparative study for vertebra segmentation methods, which will provide an up-to-date performance milestone for the fast growing spinal image analysis and intervention.

Automated generation of curved planar reformations from MR images of the spine

A novel method for automated curved planar reformation (CPR) of magnetic resonance (MR) images of the spine is presented. The CPR images, generated by a transformation from image-based to spine-based coordinate system, follow the structural shape of the spine and allow the whole course of the curved anatomy to be viewed in individual cross-sections. The three-dimensional (3D) spine curve and the axial vertebral rotation, which determine the transformation, are described by polynomial functions. The 3D spine curve passes through the centres of vertebral bodies, while the axial vertebral rotation determines the rotation of vertebrae around the axis of the spinal column. The optimal polynomial parameters are obtained by a robust refinement of the initial estimates of the centres of vertebral bodies and axial vertebral rotation. The optimization framework is based on the automatic image analysis of MR spine images that exploits some basic anatomical properties of the spine. The method was evaluated on 21 MR images from 12 patients and the results provided a good description of spine anatomy, with mean errors of 2.5 mm and 1.7 degrees for the position of the 3D spine curve and axial rotation of vertebrae, respectively. The generated CPR images are independent of the position of the patient in the scanner while comprising both anatomical and geometrical properties of the spine.

Quantitative analysis of spinal curvature in 3D: application to CT images of normal spine.

The purpose of this study is to present a framework for quantitative analysis of spinal curvature in 3D. In order to study the properties of such complex 3D structures, we propose two descriptors that capture the characteristics of spinal curvature in 3D. The descriptors are the geometric curvature (GC) and curvature angle (CA), which are independent of the orientation and size of spine anatomy. We demonstrate the two descriptors that characterize the spinal curvature in 3D on 30 computed tomography (CT) images of normal spine and on a scoliotic spine. The descriptors are determined from 3D vertebral body lines, which are obtained by two different methods. The first method is based on the least-squares technique that approximates the manually identified vertebra centroids, while the second method searches for vertebra centroids in an automated optimization scheme, based on computer-assisted image analysis. Polynomial functions of the fourth and fifth degree were used for the description of normal and scoliotic spinal curvature in 3D, respectively. The mean distance to vertebra centroids was 1.1 mm (+/-0.6 mm) for the first and 2.1 mm (+/-1.4 mm) for the second method. The distributions of GC and CA values were obtained along the 30 images of normal spine at each vertebral level and show that maximal thoracic kyphosis (TK), thoracolumbar junction (TJ) and maximal lumbar lordosis (LL) on average occur at T3/T4, T12/L1 and L4/L5, respectively. The main advantage of GC and CA is that the measurements are independent of the orientation and size of the spine, thus allowing objective intra- and inter-subject comparisons. The positions of maximal TK, TJ and maximal LL can be easily identified by observing the GC and CA distributions at different vertebral levels. The obtained courses of the GC and CA for the scoliotic spine were compared to the distributions of GC and CA for the normal spines. The significant difference in values indicates that the descriptors of GC and CA may be used to detect and quantify scoliotic spinal curvatures. The proposed framework may therefore improve the understanding of spine anatomy and aid in the clinical quantitative evaluation of spinal deformities.