Scoliosis curve type classification from 3D trunk image

Adolescent idiopathic scoliosis (AIS) is a deformity of the spine manifested by asymmetry and deformities of the external surface of the trunk. Classification of scoliosis deformities according to curve type is used to plan management of scoliosis patients. Currently, scoliosis curve type is determined based on X-ray exam. However, cumulative exposure to X-rays radiation significantly increases the risk for certain cancer. In this paper, we propose a robust system that can classify the scoliosis curve type from non invasive acquisition of 3D trunk surface of the patients. The 3D image of the trunk is divided into patches and local geometric descriptors characterizing the surface of the back are computed from each patch and forming the features. We perform the reduction of the dimensionality by using Principal Component Analysis and 53 components were retained. In this work a multi-class classifier is built with Least-squares support vector machine (LS-SVM) which is a kernel classifier. For this study, a new kernel was designed in order to achieve a robust classifier in comparison with polynomial and Gaussian kernel. The proposed system was validated using data of 103 patients with different scoliosis curve types diagnosed and classified by an orthopedic surgeon from the X-ray images. The average rate of successful classification was 93.3% with a better rate of prediction for the major thoracic and lumbar/thoracolumbar types.

Non invasive classification system of scoliosis curve types using least-squares support vector machines

Objective To determine scoliosis curve types using non invasive surface acquisition, without prior knowledge from X-ray data. Methods Classification of scoliosis deformities according to curve type is used in the clinical management of scoliotic patients. In this work, we propose a robust system that can determine the scoliosis curve type from non invasive acquisition of the 3D back surface of the patients. The 3D image of the surface of the trunk is divided into patches and local geometric descriptors characterizing the back surface are computed from each patch and constitute the features. We reduce the dimensionality by using principal component analysis and retain 53 components using an overlap criterion combined with the total variance in the observed variables. In this work, a multi-class classifier is built with least-squares support vector machines (LS-SVM). The original LS-SVM formulation was modified by weighting the positive and negative samples differently and a new kernel was designed in order to achieve a robust classifier. The proposed system is validated using data from 165 patients with different scoliosis curve types. The results of our non invasive classification were compared with those obtained by an expert using X-ray images. Results The average rate of successful classification was computed using a leave-one-out cross-validation procedure. The overall accuracy of the system was 95%. As for the correct classification rates per class, we obtained 96%, 84% and 97% for the thoracic, double major and lumbar/thoracolumbar curve types, respectively. Conclusion This study shows that it is possible to find a relationship between the internal deformity and the back surface deformity in scoliosis with machine learning methods. The proposed system uses non invasive surface acquisition, which is safe for the patient as it involves no radiation. Also, the design of a specific kernel improved classification performance.

Scoliosis Follow-Up Using Noninvasive Trunk Surface Acquisition

Adolescent idiopathic scoliosis (AIS) is a musculoskeletal pathology. It is a complex spinal curvature in a 3-D space that also affects the appearance of the trunk. The clinical follow-up of AIS is decisive for its management. Currently, the Cobb angle, which is measured from full spine radiography, is the most common indicator of the scoliosis progression. However, cumulative exposure to X-rays radiation increases the risk for certain cancers. Thus, a noninvasive method for the identification of the scoliosis progression from trunk shape analysis would be helpful. In this study, a statistical model is built from a set of healthy subjects using independent component analysis and genetic algorithm. Based on this model, a representation of each scoliotic trunk from a set of AIS patients is computed and the difference between two successive acquisitions is used to determine if the scoliosis has progressed or not. This study was conducted on 58 subjects comprising 28 healthy subjects and 30 AIS patients who had trunk surface acquisitions in upright standing posture. The model detects 93% of the progressive cases and 80% of the nonprogressive cases. Thus, the rate of false negatives, representing the proportion of undetected progressions, is very low, only 7%. This study shows that it is possible to perform a scoliotic patient's follow-up using 3-D trunk image analysis, which is based on a noninvasive acquisition technique.

Analysis of scoliosis trunk deformities using ICA

This paper describes a method for analyzing scoliosis trunk deformities using Independent Component Analysis (ICA). Our hypothesis is that ICA can capture the scoliosis deformities visible on the trunk. Unlike Principal Component Analysis (PCA), ICA gives local shape variation and assumes that the data distribution is not normal. 3D torso images of 56 subjects including 28 patients with adolescent idiopathic scoliosis and 28 healthy subjects are analyzed using ICA. First, we remark that the independent components capture the local scoliosis deformities as the shoulder variation, the scapula asymmetry and the waist deformation. Second, we note that the different scoliosis curve types are characterized by different combinations of specific independent components.

Multimodal image fusion of anatomical structures for diagnosis, therapy planning and assistance

This paper provides an overview of work done in recent years by our research group to fuse multimodal images of the trunk of patients with Adolescent Idiopathic Scoliosis (AIS) treated at Sainte-Justine University Hospital Center (CHU). We first describe our surface acquisition system and introduce a set of clinical measurements (indices) based on the trunk's external shape, to quantify its degree of asymmetry. We then describe our 3D reconstruction system of the spine and rib cage from biplanar radiographs and present our methodology for multimodal fusion of MRI, X-ray and external surface images of the trunk We finally present a physical model of the human trunk including bone and soft tissue for the simulation of the surgical outcome on the external trunk shape in AIS.

Evaluation of Reducibility of Trunk Asymmetry in Lateral Bending

The value of the lateral bending test is important in the assessment of spinal curve mobility and prediction of surgical outcome in the treatment of adolescent idiopathic scoliosis (AIS). However, radiographic bending tests are unable to assess the reducibility of trunk asymmetry. This study aims to exploit surface topography measurement in order to evaluate the changes in shape of the trunk (a) between bending and neutral standing positions, and (b) between standing pre- and post-operative visits, in a cohort of adolescents with AIS having undergone surgical correction; and to correlate the differences measured in cases (a) and (b). Our cohort includes 13 patients with right thoracic AIS. Each patient had their 3D trunk surface digitized with a multi-head InSpeck system in standing posture (at the pre-op and post-op visits) and in maximum voluntary right and left bending (at the pre-op visit). We developed a novel trunk shape analysis method which produces a set of inclined trunk cross-sections allowing comparison between different postures. Two asymmetry indices, trunk rotation (TR) and back surface rotation (BSR), were computed in all cases and a statistical analysis was performed. Our correlation study (Pearson test) showed fair correlations in most cases between the changes in side-bending and those post-surgery, with the strongest relationship (p-value < 0.01) when combining the TR measurements from both bendings. These results provide evidence that the bending test can be used to assess trunk asymmetry reducibility. The proposed approach could provide a non-invasive trunk asymmetry reducibility test for routine clinical use in AIS surgery planning.

Reliability of a quantitative clinical posture assessment tool among persons with idiopathic scoliosis

Objective To determine overall, test–retest and inter-rater reliability of posture indices among persons with idiopathic scoliosis. Design A reliability study using two raters and two test sessions. Setting Tertiary care paediatric centre. Participants Seventy participants aged between 10 and 20 years with different types of idiopathic scoliosis (Cobb angle 15 to 60°) were recruited from the scoliosis clinic. Main outcome measures Based on the XY co-ordinates of natural reference points (e.g. eyes) as well as markers placed on several anatomical landmarks, 32 angular and linear posture indices taken from digital photographs in the standing position were calculated from a specially developed software program. Generalisability theory served to estimate the reliability and standard error of measurement (SEM) for the overall, test–retest and inter-rater designs. Bland and Altman's method was also used to document agreement between sessions and raters. Results In the random design, dependability coefficients demonstrated a moderate level of reliability for six posture indices (ϕ = 0.51 to 0.72) and a good level of reliability for 26 posture indices out of 32 (ϕ ≥ 0.79). Error attributable to marker placement was negligible for most indices. Limits of agreement and SEM values were larger for shoulder protraction, trunk list, Q angle, cervical lordosis and scoliosis angles. The most reproducible indices were waist angles and knee valgus and varus. Conclusions Posture can be assessed in a global fashion from photographs in persons with idiopathic scoliosis. Despite the good reliability of marker placement, other studies are needed to minimise measurement errors in order to provide a suitable tool for monitoring change in posture over time.

Differences in Standing and Sitting Postures of Youth with Idiopathic Scoliosis from Quantitative Analysis of Digital Photographs

The objective of this study was to explore whether differences in standing and sitting postures of youth with idiopathic scoliosis could be detected from quantitative analysis of digital photographs. Standing and sitting postures of 50 participants aged 10–20-years-old with idiopathic scoliosis (Cobb angle: 15° to 60°) were assessed from digital photographs using a posture evaluation software program. Based on the XY coordinates of markers, 13 angular and linear posture indices were calculated in both positions. Paired t-tests were used to compare values of standing and sitting posture indices. Significant differences between standing and sitting positions (p < 0.05) were found for head protraction, shoulder elevation, scapula asymmetry, trunk list, scoliosis angle, waist angles, and frontal and sagittal plane pelvic tilt. Quantitative analysis of digital photographs is a clinically feasible method to measure standing and sitting postures among youth with scoliosis and to assist in decisions on therapeutic interventions.

Validity of a Quantitative Clinical Measurement Tool of Trunk Posture in Idiopathic Scoliosis

STUDY DESIGN: Concurrent validity between postural indices obtained from digital photographs (two-dimensional [2D]), surface topography imaging (three-dimensional [3D]), and radiographs. OBJECTIVE: To assess the validity of a quantitative clinical postural assessment tool of the trunk based on photographs (2D) as compared to a surface topography system (3D) as well as indices calculated from radiographs. SUMMARY OF BACKGROUND DATA: To monitor progression of scoliosis or change in posture over time in young persons with idiopathic scoliosis (IS), noninvasive and nonionizing methods are recommended. In a clinical setting, posture can be quite easily assessed by calculating key postural indices from photographs. METHODS: Quantitative postural indices of 70 subjects aged 10 to 20 years old with IS (Cobb angle, 15 degrees -60 degrees) were measured from photographs and from 3D trunk surface images taken in the standing position. Shoulder, scapula, trunk list, pelvis, scoliosis, and waist angles indices were calculated with specially designed software. Frontal and sagittal Cobb angles and trunk list were also calculated on radiographs. The Pearson correlation coefficients (r) was used to estimate concurrent validity of the 2D clinical postural tool of the trunk with indices extracted from the 3D system and with those obtained from radiographs. RESULTS: The correlation between 2D and 3D indices was good to excellent for shoulder, pelvis, trunk list, and thoracic scoliosis (0.81>r<0.97; P<0.01) but fair to moderate for thoracic kyphosis, lumbar lordosis, and thoracolumbar or lumbar scoliosis (0.30>r<0.56; P<0.05). The correlation between 2D and radiograph spinal indices was fair to good (-0.33 to -0.80 with Cobb angles and 0.76 for trunk list; P<0.05). CONCLUSION: This tool will facilitate clinical practice by monitoring trunk posture among persons with IS. Further, it may contribute to a reduction in the use of radiographs to monitor scoliosis progression.

Is Breast Asymmetry Present in Girls with Adolescent Idiopathic Scoliosis?

Study Design Cross-sectional descriptive study. Objectives To characterize breast asymmetry (BA), as defined by breast volume difference, in girls with significant adolescent idiopathic scoliosis (AIS), using magnetic resonance imaging (MRI). Summary and Background BA is a frequent concern among girls with AIS. It is commonly believed that this results from chest wall deformity. Although many women exhibit physiological BA, the prevalence is not known in adolescents and it remains unclear if it is more frequent in AIS. Breasts vary in shape and size and many ways of measuring them have been explored. MRI shows the highest precision at defining breast tissue. Methods Thirty patients were enrolled on the basis of their thoracic curvature, skeletal and breast maturity, without regard to their perception on their BA. MRI acquisitions were performed in prone with a 1.5-Tesla system using a 16-channel breast coil. Segmentation was achieved using the ITK-SNAP 2.4.0 software and subsequently manually refined. Results The mean left breast volume (528.32 ± 205.96 cc) was greater compared with the mean right breast volume (495.18 ± 170.16 cc) with a significant difference between them. The mean BA was found to be 8.32% ± 6.43% (p < .0001). A weak positive correlation was observed between BA and thoracic Cobb angle (0.177, p = .349) as well as thoracic gibbosity angle (0.289, p = .122). The left breast was consistently larger in 65.5% of the patients. Twenty patients (66.7%) displayed BA ≥5%. Conclusions We have described BA in patients with significant AIS using MRI. This method is feasible, objective, and very precise. The majority of patients had a larger left breast, which could compound the apparent BA secondary to trunk rotation. In many cases, BA is present independently of thoracic deformity. This knowledge will assist in counseling AIS patients in regards to their concerns with BA.

Towards Non Invasive Diagnosis of Scoliosis Using Semi-supervised Learning Approach

In this paper, a new methodology for the prediction of scoliosis curve types from non invasive acquisitions of the back surface of the trunk is proposed. One hundred and fifty-nine scoliosis patients had their back surface acquired in 3D using an optical digitizer. Each surface is then characterized by 45 local measurements of the back surface rotation. Using a semi-supervised algorithm, the classifier is trained with only 32 labeled and 58 unlabeled data. Tested on 69 new samples, the classifier succeeded in classifying correctly 87.0% of the data. After reducing the number of labeled training samples to 12, the behavior of the resulting classifier tends to be similar to the reference case where the classifier is trained only with the maximum number of available labeled data. Moreover, the addition of unlabeled data guided the classifier towards more generalizable boundaries between the classes. Those results provide a proof of feasibility for using a semi-supervised learning algorithm to train a classifier for the prediction of a scoliosis curve type, when only a few training data are labeled. This constitutes a promising clinical finding since it will allow the diagnosis and the follow-up of scoliotic deformities without exposing the patient to X-ray radiations.

Noninvasive Clinical Assessment of Trunk Deformities Associated With Scoliosis

Besides the spinal deformity, scoliosis modifies notably the general appearance of the trunk resulting in trunk rotation, imbalance, and asymmetries that constitutes patients' major concern. Existing classifications of scoliosis, based on the type of spinal curve as depicted on radiographs, are currently used to guide treatment strategies. Unfortunately, even though a perfect correction of the spinal curve is achieved, some trunk deformities remain, making patients dissatisfied with the treatment received. The purpose of this study is to identify possible shape patterns of trunk surface deformity associated with scoliosis. First, trunk surface is represented by a multivariate functional trunk shape descriptor based on 3-D clinical measurements computed on cross sections of the trunk. Then, the classical formulation of hierarchical clustering is adapted to the case of multivariate functional data and applied to a set of 236 trunk surface 3-D reconstructions. The highest internal validity is obtained when considering 11 clusters that explain up to 65% of the variance in our dataset. Our clustering result shows a concordance with the radiographic classification of spinal curves in 68% of the cases. As opposed to radiographic evaluation, the trunk descriptor is 3-D and its functional nature offers a compact and elegant description of not only the type, but also the severity and extent of the trunk surface deformity along the trunk length. In future work, new management strategies based on the resulting trunk shape patterns could be thought of in order to improve the esthetic outcome after treatment, and thus patients satisfaction.

Prediction of scoliosis curve type based on the analysis of trunk surface topography

Scoliosis treatment strategy is generally chosen according to the severity and type of the spinal curve. Currently, the curve type is determined from X-rays whose acquisition can be harmful for the patient. We propose in this paper a system that can predict the scoliosis curve type based on the analysis of the surface of the trunk. The latter is acquired and reconstructed in 3D using a non invasive multi-head digitizing system. The deformity is described by the back surface rotation, measured on several cross-sections of the trunk. A classifier composed of three support vector machines was trained and tested using the data of 97 patients with scoliosis. A prediction rate of 72.2% was obtained, showing that the use of the trunk surface for a high-level scoliosis classification is feasible and promising.

Development of Shape Descriptors Based on Legendre Polynomials and Content-Based Retrieval of Scoliosis Images

The wealth of information available freely on the web and medical image databases poses a major problem for the end users: how to find the information needed? Content –Based Image Retrieval is the obvious solution.A standard called MPEG-7 was evolved to address the interoperability issues of content-based search.The work presented in this thesis mainly concentrates on developing new shape descriptors and a framework for content – based retrieval of scoliosis images.New region-based and contour based shape descriptor is developed based on orthogonal Legendre polymomials.A novel system for indexing and retrieval of digital spine radiographs with scoliosis is presented.