Integrated Approach for Cobb Angle Estimation in X-Ray C-Curve Scoliosis Using Image Processing and Inverse Cosine Law

Cobb angle is the angle subtended by the most tilted vertebrae. It represents the degree of the spinal curvature and has been used to diagnose scoliosis. The severity of the scoliosis based on the cobb angle will determine the guide treatment and surgical planning. Manual measurement of Cobb angle using protractor or semi-manual using ONIS software is quite time consuming, and it is subjected to variations in interobserver and intraobserver measurements. This research proposed an algorithm to determine the Cobb angle using image processing and inverse cosine methods. The raw samples of X-ray images were obtained from Pusat Kesihatan Universiti (PKU), UTHM.  Several interviews were conducted with radiologist at Pantai Hospital for verification purpose. Gaussian blur and unsharp mask were initially applied for noise removal in the preprocessing step. Three points were marked at the vertebrae image to assist for centroid estimation using Haar Cascade. The Cobb angle was estimated by applying the triangle formula derived from the inverse cosine law. In comparison to semi-manual method (Onis Software), the proposed technique has an error of less than 5 % and computes the Cobb angle 3.28 times quicker. In the future, it is suggested to explore other techniques such as deep learning for alternative data analysis for Cobb angle estimation

A Survey on 3D Ultrasound Reconstruction Techniques

This book chapter aims to discuss the 3D ultrasound reconstruction and visualization. First, the various types of 3D ultrasound system are reviewed, such as mechanical, 2D array, position tracking-based freehand, and untracked-based freehand. Second, the 3D ultrasound reconstruction technique or pipeline used by the current existing system, which includes the data acquisition, data preprocessing, reconstruction method and 3D visualization, is discussed. The reconstruction method and 3D visualization will be emphasized. The reconstruction method includes the pixel-based method, volume-based method, and function-based method, accompanied with their benefits and drawbacks. In the 3D visualization, methods such as multiplanar reformatting, volume rendering, and surface rendering are presented. Lastly, its application in the medical field is reviewed as well

Non-invasive methods of computer vision in the posture evaluation of adolescent idiopathic scoliosis

Purpose: Reviewing techniques for non-invasive postural evaluation of adolescent idiopathic scoliosis (AIS) based on information extraction from images based on computer methods. Methods: The Scopus, Web of Science, MEDLINE, ScieLo and PubMed databases were used, for the period 2011-2015. Results: 131 articles were found based on keyword of which 15 articles met the established eligibility criteria. Of these, 4 were based on photogrammetry, and 11 based on laser, structured light, ultrasound, and Moire projection. In these studies, the methodological quality varied from low to high. Conclusions: The findings indicated diversity in methodologies; 14/15 articles reviewed were limited to the evaluation of the topography of the posterior back. A study, using two-dimensional photogrammetry, presented a whole body postural evaluation. As the asymmetry in AIS can be extended to the whole body, more attention should be given to develop full body assessment techniques to provide important additional data to aid in treatment decisio

Modélisation géométrique 3D des structures anatomiques du tronc humain à partir d’images acquises par résonnance magnétique

La modélisation géométrique 3D de structures anatomiques est une étape essentielle dans le développement d’outils de simulation numérique dédiés pour l’étude de l’évolution ou pour la planification de traitements de pathologies complexes. La scoliose est une déformation complexe de la colonne vertébrale et de la cage thoracique qui entraine des asymétries au niveau de l’ensemble du tronc. Ces asymétries sont généralement accompagnées de l’apparence d’une bosse dans le dos du patient et constituent la raison principale pour laquelle le patient ou ses parents décident de consulter. Cependant, les simulateurs biomécaniques actuels se concentrent sur le choix de la meilleure stratégie opératoire qui permet de redresser la colonne et minimiser son déjettement au niveau sagittal et frontal. Dans ce contexte, une modélisation géométrique 3D des structures osseuses est suffisante. Par contre, la priorité du patient est de bénéficier de la stratégie qui pourrait améliorer son apparence par la réduction des asymétries externes du tronc suite au traitement. Il est donc important de propager la correction des structures osseuses, lors de la simulation, à travers les tissus mous du tronc afin de visualiser l’effet d’une stratégie sur l’apparence externe du patient. Par conséquent, une modélisation géométrique précise de l’ensemble des structures anatomiques du tronc incluant la surface externe, les tissus mous et les structures osseuses sous-jacentes devient indispensable. La modélisation de l’intérieur du tronc peut être effectuée en utilisant des images acquises par résonnance magnétique (IRM). Cette modalité d’imagerie est particulièrement intéressante, car elle permet d’obtenir de l’information sur le tronc sans danger pour le patient. La qualité des données IRM est variable et dépend du protocole d’acquisition. Pour garder un temps d’acquisition raisonnable, il faut réduire la portion du tronc couverte ou réduire la résolution des données, ce qui impactera le modèle géométrique obtenu. De plus, puisque les structures osseuses ne sont pas facilement identifiables dans les données IRM, elles sont généralement obtenues avec des radiographies. L’obtention d’un modèle précis du tronc implique donc de combiner un modèle des structures osseuses et un des tissus mous. Cette mise en correspondance est complexe, car les IRM sont acquises en position couchée et les radiographies le sont en position debout. Cette thèse propose une nouvelle méthodologie pour construire un modèle géométrique précis et personnalisé du tronc à partir de données IRM. Le nouveau modèle géométrique sera obtenu sans segmenter les données pour éviter la perte d’information. Cette méthodologie est différente des approches classiques qui génèrent des éléments géométriques reliant des frontières segmentées dans une étape préalable. Le nouveau modèle sera enrichi par l’utilisation de modèles surfaciques de vertèbres qui permettront une segmentation automatique des vertèbres visibles dans les données IRM. La première phase des travaux s’est concentrée sur la génération du modèle géométrique personnalisé du tronc obtenu à travers l’adaptation d’un maillage 3D. Le processus d’adaptation du maillage est basé sur la génération d’une métrique riemannienne construite en utilisant l’intensité des images IRM. La métrique définit la forme, la taille et l’orientation de chacun des éléments du maillage pour respecter les frontières des structures présentes dans les données. La validation du processus a été effectuée en plusieurs étapes. Tout d’abord, il a été montré, avec des IRM cardiaques, que le processus produit des maillages respectant la métrique. Par la suite, le processus d’adaptation a été comparé avec celui proposé par Goksel et al qui produit également des maillages sans segmenter les données. Cette comparaison a été faite sur un cas analytique et sur une série de cas réels. Pour comparer les méthodes, plusieurs maillages de densités différentes sont obtenus avec chacune d’elles. Puis, des éléments sont extraits de chacun des maillages en utilisant la frontière d’un volume de référence. La somme du volume des éléments extraits est comparée à celui de la référence. Les mesures comparant les volumes confirment que notre méthode produit des maillages respectant mieux les frontières des structures présentes, qu’elle converge plus rapidement et qu’elle est donc plus précise pour un nombre de sommets donnés. La seconde phase a été centrée sur le développement d’une méthodologie de segmentation semi-automatique des vertèbres dans les données IRM. Un modèle surfacique des structures osseuses est recalé avec les volumes de données IRM pour segmenter les vertèbres. Pour y parvenir, un algorithme de recalage par information mutuelle, reconnu pour donner de bons résultats avec des données multimodales, a été utilisé. Pour améliorer le taux de succès de l’algorithme, une phase d’initialisation positionne les vertèbres près de leur position finale estimée. L’évaluation de la phase d’initialisation montre que l’algorithme de recalage supporte une erreur de positionnement de 13 mm par rapport à sa position finale pour assurer un bon recalage. Cette distance est facilement atteignable. La robustesse de l’algorithme de recalage a été évaluée avec plusieurs ensembles de données. Si la qualité des données IRM est suffisante, notre méthode produit de bons résultats. Une résolution de 3 mm entre les tranches est un bon compromis entre la qualité et le temps d’acquisition. Pour conclure, la nouvelle représentation géométrique est minimale et préserve la frontière des structures anatomiques présentes dans les données. Elle serait un bon candidat pour être utilisée dans un simulateur numérique. En outre, la méthode de segmentation semi-automatique des données IRM est robuste et produit des résultats fiables. Pour poursuivre ces travaux, la segmentation des vertèbres pourrait être utilisée pour simplifier la génération du maillage. L’adaptation de maillage peut être restreinte à des zones segmentées, tout en utilisant l’information du volume entier, limitant ainsi la perte d’information. L’emplacement des vertèbres serait alors connu dans le maillage adapté, ce qui permettrait de faire le recalage avec le modèle surfacique des structures osseuses.----------ABSTRACT 3D geometric modeling of anatomical structures is an essential step in the development of numerical simulation tools dedicated to the study of evolution or the planning of complex disease treatments. Scoliosis is a complex deformation of the spine and rib cage which leads to asymmetries in the whole trunk. These asymmetries are usually accompanied by the appearance of a hump in the back of the patient and are the main reason why the patient or his parents decide to consult. However, current biomechanical simulators focus on choosing the best surgical strategy that helps straighten the spine and achieve frontal and sagittal trunk balance. In this context, a 3D geometric modeling of bone structures is sufficient. On the other hand, the priority of the patient is to benefit from the strategy that could improve most his appearance by reducing trunk asymmetries. It is therefore important to propagate the correction of bone structures, in the simulation, through the soft tissue of the trunk, in order to visualize the effect of a strategy on the external surface of the trunk. Therefore, a precise geometric modeling of all anatomical structures of the trunk including the outer surface, the soft tissue and underlying bone structures becomes essential. Modeling the inside of the trunk may be performed using images acquired by magnetic resonance imaging (MRI). This imaging modality is particularly interesting because it provides information on the trunk without any danger for the patient. The quality of MRI data is variable and depends on the acquisition protocol. To keep a reasonable time of acquisition, either the scope of the trunk or the resolution of the data has to be reduced, but this has an impact on the quality of the resulting geometric model. In addition, since the bone structures are not easily identifiable in the MRI data, they are generally obtained with radiographs. Obtaining an accurate model of the trunk therefore involves combining a model of bone structures and a model of soft tissues. Combining those models is complex because MRI are acquired in a laying position and the radiographs are acquired in a standing position. This thesis proposes a new methodology to build a precise and personalised geometric model of the trunk based on MRI data. The new model will be obtained without segmenting the data to avoid any loss of information. This methodology is different of the standard approaches that produce geometric elements linking boundaries segmented in an initial step. The new model will be enhanced with the use of surfacic models of vertebrae to perform an automatic segmentation of the visible vertebra within the MRI dataset. The first phase of our work has focused on the generation of a custom geometric model of the trunk obtained through the adaptation of a 3D mesh. The mesh adaptation process is based on the generation of a Riemannian metric constructed using the grey levels of the MRI data. The metric defines the shape, size and orientation of each mesh element to respect the boundaries of anatomical structures in the data. The validation process was performed in several steps. Firstly, it has been shown, with cardiac MRI, that the process produces meshes respecting the metric. Thereafter, the adaptation process was compared with the one proposed by Goksel et al which also produces meshes without segmenting the data. This comparison was made on an analytical case and a series of real cases. To compare the methods, several meshes with different densities were obtained with each of them. Then, elements were extracted from each of the meshes using the boundary of a reference volume. The sum of the volume of the extracted elements was compared with the reference. Measurements comparing the volumes confirmed that our method produces meshes respecting the boundaries of the structures better, that converges faster and is therefore more accurate for a given number of vertices The second phase focused on the development of a methodology for semi-automatic segmentation of the vertebrae in MRI data. A surface model of bone structures is registered with MRI data volumes to segment vertebrae. To achieve this, a registration based on a mutual information algorithm, known to give good results with multimodal data, was used. To improve the success rate of the algorithm, an initialization phase positions the vertebrae near their estimated final position. The evaluation of the initialization phase shows that the registration algorithm supports a positioning error of 13 mm from its final position to ensure proper registration. This distance is easily attainable. The robustness of the registration algorithm was evaluated with multiple data sets. If MRI data quality is adequate, our method produces good results. A resolution of 3 mm between slices is a good compromise between data quality and acquisition time. In conclusion, the new geometric representation is minimal and preserves the border of anatomical structures in the data. It would be a good candidate to be used for simulations. In addition, the semi-automatic segmentation method of MRI data is robust and produces reliable results. To continue this work, segmentation of the vertebrae could be used to simplify the generation of the mesh. Mesh adaptation may be restricted to segmented areas while using the information of the entire volume, hence limiting information loss. The location of the vertebrae would be known in the adapted mesh, thereby simplifying the registration with the surface model of the bone structures

Shape analysis for assessment of progression in spinal deformities

Adolescent idiopathic scoliosis (AIS) is a three-dimensional structural spinal deformation. It is the most common type of scoliosis. It can be visually detected as a lateral curvature in the postero-anterior plane. This condition starts in early puberty, affecting between 1-4% of the adolescent population between 10-18 years old, affecting in majority female. In severe cases (0.1% of population with AIS) the patient will require a surgical treatment. To date, the diagnosis of AIS relies on the quantification of the major curvature observed on posteroanterior and sagittal radiographs. Radiographs in standing position are the common imaging modality used in clinical settings to diagnose AIS. The assessment of the deformation is carried out using the Cobb angle method. This angle is calculated in the postero-anterior plane, and it is formed between a line drawn parallel to the superior endplate of the upper vertebra included in the scoliotic curve and a line drawn parallel to the inferior endplate of the lower vertebra of the same curve. Patients that present a Cobb angle of more than 10°, are diagnosed with AIS. The gold standard to classify curve deformations is the Lenke classification method. This paradigm is widely accepted in the clinical community. It divides spines with scoliosis into six types and provides treatment recommendations depending on the type. This method is limited to the analysis of the spine in the 2D space, since it relies on the observation of radiographs and Cobb angle measurements. On the one hand, when clinicians are treating patients with AIS, one of the main concerns is to determine whether the deformation will progress through time. Knowing beforehand of how the shape of the spine is going to evolve would aid to guide treatments strategies. On the other hand, however, patients at higher risks of progression require to be monitored more frequently, which results in constant exposure to radiation. Therefore, there is a need for an alternative radiation-free technology to reduce the use of radiographs and alleviate the perils of other health issues derived from current imaging modalities. This thesis presents a framework designed to characterize and model the variation of the shape of the spine throughout AIS. This framework includes three contributions: 1) two measurement techniques for computing 3D descriptors of the spine, and a classification method to categorize spine deformations, 2) a method to simulate the variation of the shape of the spine through time, and 3) a protocol to generate a 3D model of the spine from a volume reconstruction produced from ultrasound images. In our first contribution, we introduced two measurement techniques to characterize the shape of the spine in the 3D space, leave-n-out, and fan leave-n-out angles. In addition, a dynamic ensemble method was presented as an automated alternative to classify spinal deformations. Our measurement techniques were designed for computing the 3D descriptors and to be easy to use in a clinical setting. Also, the classification method contributes by assisting clinicians to identify patient-specific descriptors, which could help improving the classification in borderline curve deformations and, hence, suggests the proper management strategies. In order to observe how the shape of the spine progresses through time, in our second contribution, we designed a method to visualize the shape’s variation from the first visit up to 18 months, for every three months. Our method is trained with modes of variation, computed using independent component analysis from 3D model reconstructions of the spine of patients with AIS. Each of the modes of variation can be visualized for interpretation. This contribution could aid clinicians to identify which spine progression pattern might be prone to progression. Finally, our third contribution addresses the necessity of a radiation-free image modality for assessing and monitoring patients with AIS. We proposed a protocol to model a spine by identifying the spinous processes on a volume reconstruction. This reconstruction was computed from ultrasound images acquired from the external geometry of the subject. Our acquisition protocol documents a setup for image acquisition, as well as some recommendations to take into account depending on the body composition of the subjects to be scanned. We believe that this protocol could contribute to reduce the use of radiographs during the assessment and monitoring of patients with AIS

Investigating the ability to use the CT scan projection radiograph to monitor adolescent idiopathic scoliosis

Introduction: Adolescent idiopathic scoliosis (AIS) is a spinal deformity that causes the spine to bend laterally. Patients with AIS undergo frequent X-ray examinations to monitor the progression of the deformity through the measurement of the Cobb angle, increasing the risk of developing radiation-induced cancer. The aim of this study was to investigate the use of scan projection radiograph (SPR) in computed tomography (CT) to assess AIS by quantifying radiation dose from the SPR acquisitions and comparing it to those of digital radiography (DR) and a dedicated scoliosis imaging system (EOS) and by evaluating the accuracy of Cobb angle measurements on SPR images using a bespoke validated phantom. Methods: A dosimetry phantom representing a 10-year-old child and thermoluminescent dosimeters were used for measuring organ dose to calculate effective dose (ED) and effective risk (ER). Twenty-seven CT SPR protocols were used. A comparison was made to doses from imaging protocols using DR and the EOS system. The effectiveness of a scoliosis shawl for selected projections was also tested. To test the accuracy of Cobb angle measurements on SPR images, a scoliotic phantom was constructed and validated. Poly-methyl methacrylate (PMMA) and plaster of Paris (PoP) were used to represent human soft tissue and bone tissue, respectively, to construct a phantom exhibiting a 15° lateral curve of the spine. The phantom was validated by comparing the Hounsfield unit (HU) of its vertebrae with those of a human and an animal. Additionally, comparisons of signal-to-noise ratio (SNR) to those from a commercially available phantom were made. The angle of the curve in the phantom was measured directly to confirm that it was 15°. The constructed phantom was scanned in CT SPR mode, and the resulting images were visually evaluated against set criteria to determine their suitability for Cobb angle measurements. Those deemed of insufficient quality were excluded. Cobb angle measurements were then performed on the remaining images (n = 10) by 13 observers.Results: EOS had the lowest ED and ER when it was used to irradiate the phantom in AP positions. Five SPR AP imaging protocols and seven PA imaging protocols delivered significantly lower radiation dose and risk than their corresponding imaging positions in DR (p < 0.05). The scoliosis shawl significantly lowered the ED and ER of SPR and DR AP imaging protocols (p < 0.05). The validation of the PoP phantom revealed that the HU of the PoP vertebrae was 628 (SD= 56), human vertebrae was 598 (SD= 79) and sheep vertebra was 605 (SD= 83). The SNR values of the two phantoms correlated strongly (r = 0.93 [(p < 0.05]). The measured scoliosis angle was 14 degrees. When the phantom was imaged using SPR, the difference between the measured Cobb angle and the known angle was, on average, –2.75° (SD = 1.46°). The agreement among the observers was good (p = 0.861, 95% CI [0.70–0.95]) and comparable to similar studies on other imaging modalities which are used for Cobb angle estimation.Conclusion: EOS had the lowest dose. Where this technology is not available, there is a potential for organ dose (OD) reduction in AIS imaging using CT SPR compared with DR. The PoP phantom has physical characteristics (in terms of spinal deformity) and radiological characteristics (in terms of HU and SNR values) of the spine of a 10-year-old child with AIS. CT SPR images can be used for AIS assessment with the 5° margin of error that is clinically acceptable. A few SPR imaging protocols (CT4, 8 and 11) had the lower radiation risk compared with the DR and provided the most accurate Cobb angle measurements.Implications for practice: The bespoke phantom can be used to investigate new X-ray imaging techniques and technology in the assessment of scoliosis and has utility for the optimisation of X-ray imaging techniques in 10-year-old children. Overall, the outcome is promising for patients and health providers because it provides an opportunity to reduce patient dose and achieve clinically acceptable Cobb angle measurements whilst using existing CT technology