A Framework on A Computer Assisted and Systematic Methodology for Detection of Chronic Lower Back Pain using Artificial Intelligence and Computer Graphics Technologies

Back pain is one of the major musculoskeletal pain problems that can affect many people and is considered as one of the main causes of disability all over the world. Lower back pain, which is the most common type of back pain, is estimated to affect at least 60% to 80% of the adult population in the United Kingdom at some time in their lives. Some of those patients develop a more serious condition namely Chronic Lower Back Pain in which physicians must carry out a more involved diagnostic procedure to determine its cause. In most cases, this procedure involves a long and laborious task by the physicians to visually identify abnormalities from the patient’s Magnetic Resonance Images. Limited technological advances have been made in the past decades to support this process. This paper presents a comprehensive literature review on these technological advances and presents a framework of a methodology for diagnosing and predicting Chronic Lower Back Pain. This framework will combine current state-of-the-art computing technologies including those in the area of artificial intelligence, physics modelling, and computer graphics, and is argued to be able to improve the diagnosis process

New Algorithm For Detection of Spinal Cord Tumor using OpenCV

The spinal cord one of the most sensitive and significant parts of the human body lies protected inside the spine the backbone and contains bundles of nerves Any minor problem in the spinal cord can cause debilitation of internal and external functions of the human body One of the complications in the spinal cord is tumor - abnormal growth of tissue In this project we present a new algorithm based on OpenCV to detect spinal cord tumors from MRI sagittal image without human intervention The new algorithm can detect tumor-like substances adjacent to the spinal cord Tests carried out on spinal cord MRI images 33 cervical spinal images showed approximately 90 91 of accuracy rate in detecting tumor

A Machine Learning and Computer Assisted Methodology for Diagnosing Chronic Lower Back Pain on Lumbar Spine Magnetic Resonance Images

Chronic Lower Back Pain (CLBP) is one of the major types of pain that affects many people around the world. It is estimated that 28.1% of US adults suffer from this illness and 2.5 million of the UK population experience this type of pain every day. Most CLBP cases do not happen overnight and it is usually developed from a less serious but acute variant of lower back pain. An acute type of lower back pain can develop into a chronic one if the underlying cause is serious and left untreated. The longer a person is disabled by back pain, the less chance he or she returns to work and the more health care cost he or she will require. It is therefore important to identify the cause of back pains as early as possible in order to improve the chance of patient rehabilitation. The speediness of early diagnosis can depend on many factors including referral time from a general practitioner to the hospital, waiting time for a specialist appointment, time for a Magnetic Resonance Imaging (MRI) scan and time for the analysis result to come out. Currently diagnosing the lower back pain is done by visual observation and analysis of the lumbar spine MRI images by radiologists and clinicians and this process could take up much of their time and effort. This, therefore, rationalizes the need for a new method to increase the efficiency and effectiveness of the imaging diagnostic process. This thesis details a novel methodology to automatically aid clinicians in performing diagnosis of CLBP on lumbar spine MRI images. The methodology is based on the current accepted medical practice of manual inspection of the MRI scans of the patient’s lumbar spine as advised by several practitioners in this field. The main methodology is divided into three sub-methods the first sub-method is disc herniation detection using disc segmentation and centroid distance function. While the second sub-method is lumbar spinal stenosis detection via segmentation of area between anterior and posterior (AAP) Elements. Whereas, the last sub-method is the use of deep learning to perform semantic segmentation to identify regions in the MRI images that are relevant to the diagnosis process. The method then performs boundary delineation between these regions, identifies key points along the boundaries and measures distances between these points that can be used as an indication to the health of the lumbar spine. Due to a limitation in the size and suitability of the currently existing open-access lumbar spine dataset necessary to train and test any good classification algorithms, a dataset consisting of 48,345 MRI slices from a complete clinical lumbar MRI study of 515 symptomatic back pain patients from several specialty hospitals around the world has been created. Each MRI study is annotated by expert radiologists with notes regarding the observed characteristics, condition of the lumbar spine, or presence of diseases. The ground-truth dataset containing manually labelled segmented images has also been developed. To complement this ground-truth dataset, a novel method of constructing and evaluating the suitability of ground truth data for lumbar spine MRI image segmentation has been developed. A subset of the dataset, which includes the data for 101 patients, is used in a set of experiments that have been conducted using a variety of algorithms to conclude with using SegNet as the image segmentation algorithm. The network consists of VGG16 layers pre-trained using a subset of non-medical images from the ImageNet database and fine-tuned using the training portion of the ground-truth dataset. The results of these experiments show the accurate delineation of important boundaries of regions in lumbar spine MRI. The experiments also show very close agreement between the expert radiologists’ notes on the condition of a lumbar spine and the conclusion of the system about the lumbar spine in the majority of cases

Statistical shape model reconstruction with sparse anomalous deformations: application to intervertebral disc herniation

Many medical image processing techniques rely on accurate shape modeling of anatomical features. The presence of shape abnormalities challenges traditional processing algorithms based on strong morphological priors. In this work, a sparse shape reconstruction from a statistical shape model is presented. It combines the advantages of traditional statistical shape models (defining a ‘normal’ shape space) and previously presented sparse shape composition (providing localized descriptors of anomalies). The algorithm was incorporated into our image segmentation and classification software. Evaluation was performed on simulated and clinical MRI data from 22 sciatica patients with intervertebral disc herniation, containing 35 herniated and 97 normal discs. Moderate to high correlation (R = 0.73) was achieved between simulated and detected herniations. The sparse reconstruction provided novel quantitative features describing the herniation morphology and MRI signal appearance in three dimensions (3D). The proposed descriptors of local disc morphology resulted to the 3D segmentation accuracy of 1.07 ± 1.00 mm (mean absolute vertex-to-vertex mesh distance over the posterior disc region), and improved the intervertebral disc classification from 0.888 to 0.931 (area under receiver operating curve). The results show that the sparse shape reconstruction may improve computer-aided diagnosis of pathological conditions presenting local morphological alterations, as seen in intervertebral disc herniation

Procedures for finite element mesh generation from medical imaging: application to the intervertebral disc

Dissertação de mestrado integrado em Engenharia BiomédicaThe paramount goal of this ‘half-year’ work is the development of a set of methodologies and procedures for the geometric modelling by a finite element (FE) mesh of the bio-structure of a motion segment (or functional spinal unit), i.e., two vertebrae and an intervertebral disc, from segmented medical images (processed from medical imaging). At an initial stage, a three-dimensional voxel-based geometric model of a goat motion segment was created from magnetic resonance imaging (MRI) data. An imaging processing software (ScanIP/Simplewire) was used for imaging segmentation (identification of different structures and tissues), both in images with lower (normal MRI) and higher (micro-MRI) resolutions. It shall be noticed that some soft-tissues, such as annulus fibrosus or nucleus pulposus, are very hard to isolate and identify given that the interface between them is not clearly defined. At the end of this stage, images with different resolutions allowed to generate different 3D voxel-based geometric models. Thereafter, a procedure for the FE mesh generation from the aforementioned voxelized data should be studied and applied. However, as the original geometry was only approximately known from real medical imaging, it was difficult to objectively quantify the quality of the FE meshing procedure and the accuracy between source geometry and target FE mesh. In order to overcome such difficulties, and due to the lack of quality of the available medical imaging, a “virtualization” procedure was developed to create a set of segmented 2D medical images from a well-defined geometry of a motion segment. The main idea was to create the conditions to quantify the quality and the accuracy of the developed FE meshing procedure, as well to study the effect of imaging resolution. Starting from the virtually generated 2D segmented images, a 3D voxel-based structure was achieved. Given that initial domains are now clearly defined, there is no need for further image processing. Then, a two-step FE mesh generation procedure (generation followed by simplification) allows to create an optimized tetrahedral FE mesh directly from 3D voxelized data. Finally, because the virtualization procedure allowed to know the initial geometry, one is able to objectively quantify the quality and the accuracy of the final simplified tetrahedral FE mesh, and thus to understand and quantify: a) the role of the medical image resolution on the FE geometrical reconstruction, b) the procedure and parameters of the FE mesh generation step, and c) the procedure and parameters of the FE mesh simplification step, and thus to give a clear contribution in the definition of the procedure for the FE mesh generation from medical imaging in case of an intervertebral disc.O objetivo fundamental deste trabalho de seis meses é o desenvolvimento de um conjunto de metodologias e procedimentos para a modelação geométrica, através de uma malha de elementos finitos (EF) de uma bio-estrutura de um motion segment (ou unidade funcional da coluna), ou seja, duas vértebras e um disco intervertebral, a partir de imagens médicas segmentadas (processadas a partir de imagiologia médica). Numa fase inicial, um modelo geométrico tridimensional baseado em voxels de um motion segment de uma cabra foi criado a partir de informação de imagens médicas de ressonância magnética (RM). Um software de processamento de imagem (ScanIp/Simplewire) foi usado para segmentação de imagens (identificação de diferentes estruturas e tecidos), em imagens de menor (RM normal) e maior (micro-RM) resolução. Deve ser referido que alguns tecidos moles, como o anel fibroso e o núcleo pulposo são muito difíceis de isolar e identificar, dado que as fronteiras destes não estão claramente definidas. No final desta etapa, as imagens com diferentes resoluções permitiram gerar diferentes modelos geométricos 3D baseados em voxels. Posteriormente, um procedimento para geração de malha de EF, a partir da informação voxelizada acima mencionada, deveria ser estudado e aplicado. No entanto, como a geometria original era aproximadamente conhecida a partir de imagens médicas reais, foi difícil quantificar objetivamente a qualidade do procedimento de geração de malha de EF e a precisão entre a geometria de origem e a malha de EF de destino. A fim de superar tais dificuldades, e devido à falta de qualidade de imagens médicas disponíveis, um procedimento de “virtualização” foi desenvolvido para criar um conjunto de imagens médicas 2D segmentadas a partir de uma geometria de um motion segment bem conhecida. A principal ideia foi criar as condições para quantificar a qualidade e a precisão do procedimento de geração de malha de EF desenvolvido, bem como estudar o efeito da resolução da imagem médica. A partir das imagens 2D segmentadas, geradas virtualmente, uma estrutura de voxels 3D pode ser conseguida. Dado que os domínios iniciais estão agora claramente definidos, não há necessidade de processamento de imagem adicional. Por conseguinte, um procedimento de geração de malha de EF de duas etapas (geração seguida por simplificação) permite criar uma malha de EF tetraédrica otimizada diretamente a partir de informação 3D voxelizada. Por fim, como o procedimento de virtualização permitiu conhecer a geometria inicial, é possível quantificar objetivamente a qualidade e exatidão da malha de EF tetraédrica final simplificada, e assim, compreender e quantificar: a) o papel da resolução da imagem médica na reconstrução geométrica de EF; b) o procedimento e os parâmetros da etapa de geração de malha de EF; c) o procedimento e os parâmetros da etapa de simplificação de malhas de EF, e assim, dar uma contribuição clara na definição do procedimento para a geração de malha de EF a partir de imagem médica, no caso de um disco intervertebral.European Project : NP Mimetic - Biomimetic Nano-Fiber Based Nucleus Pulposus Regeneration for the Treatment of Degenerative Disc Disease, funded by the European Commission under FP7 (grant NMP3-SL-2010-246351

MRI signal distribution within the intervertebral disc as a biomarker of adolescent idiopathic scoliosis and spondylolisthesis

Background: Early stages of scoliosis and spondylolisthesis entail changes in the intervertebral disc (IVD) structure and biochemistry. The current clinical use of MR T2-weighted images is limited to visual inspection. Our hypothesis is that the distribution of the MRI signal intensity within the IVD in T2-weighted images depends on the spinal pathology and on its severity. Therefore, this study aims to develop the AMRSID (analysis of MR signal intensity distribution) method to analyze the 3D distribution of the MR signal intensity within the IVD and to evaluate their sensitivity to scoliosis and spondylolisthesis and their severities. Methods: This study was realized on 79 adolescents who underwent a MRI acquisition (sagittal T2-weighted images) before their orthopedic or surgical treatment. Five groups were considered: low severity scoliosis (Cobb angle 50 degrees), low severity spondylolisthesis (Meyerding grades I and II), high severity spondylolisthesis (Meyerding grades III, IV and V) and control. The distribution of the MRI signal intensity within the IVD was analyzed using the descriptive statistics of histograms normalized by either cerebrospinal fluid or bone signal intensity, weighted centers and volume ratios. Differences between pathology and severity groups were assessed using one-and two-way ANOVAs. Results: There were significant (p < 0.05) variations of indices between scoliosis, spondylolithesis and control groups and between low and high severity groups. The cerebrospinal fluid normalization was able to detect differences between healthy and pathologic IVDs whereas the bone normalization, which reflects both bone and IVD health, detected more differences between the severities of these pathologies. Conclusions: This study proves for the first time that changes in the intervertebral disc, non visible to the naked eye on sagittal T2-weighted MR images of the spine, can be detected from specific indices describing the distribution of the MR signal intensity. Moreover, these indices are able to discriminate between scoliosis and spondylolisthesis and their severities, and provide essential information on the composition and structure of the discs whatever the pathology considered. The AMRSID method may have the potential to complement the current diagnostic tools available in clinics to improve the diagnostic with earlier biomarkers, the prognosis of evolution and the treatment options of scoliosis and spondylolisthesis

AI MSK clinical applications: spine imaging

Recent investigations have focused on the clinical application of artificial intelligence (AI) for tasks specifically addressing the musculoskeletal imaging routine. Several AI applications have been dedicated to optimizing the radiology value chain in spine imaging, independent from modality or specific application. This review aims to summarize the status quo and future perspective regarding utilization of AI for spine imaging. First, the basics of AI concepts are clarified. Second, the different tasks and use cases for AI applications in spine imaging are discussed and illustrated by examples. Finally, the authors of this review present their personal perception of AI in daily imaging and discuss future chances and challenges that come along with AI-based solutions

Lien entre les pathologies rachidiennes et l’intensité de signal IRM dans le disque intervertébral

RÉSUMÉ La scoliose et le spondylolisthésis sont des pathologies rachidiennes qui touchent respectivement 1,5-3% et 13.6% des personnes et possèdent un potentiel d’évolution. Ces pathologies tridimensionnelles sont principalement étudiées par des indices géométriques bidimensionnels ne reflétant qu’une partie des modifications morphologiques et biomécaniques/biochimiques du rachis. L’étude clinique de ces pathologies et de leurs évolutions sont réalisées à partir d’informations limitées sur les modifications engendrées au rachis. Le disque intervertébral (DIV) est le tissu mou permettant la mobilité entre les segments vertébraux. Il subit les effets dégénératifs des pathologies de manière précoce en son sein. Notre étude se base sur l’hypothèse que l’intensité de signal IRM au sein d’images cliniques pondérées en T2 est sensible à la pathologie rachidienne et à sa sévérité, permettant ainsi à cette modalité d’imagerie de fournir des informations sur les propriétés géométriques, biochimiques et mécaniques du DIV, et donc de réaliser des suivis in-vivo de l’évolution des pathologies rachidiennes. Ce projet vise à développer des techniques permettant l’étude tridimensionnelle de la distribution géométrique et gaussienne du signal IRM pondéré en T2 au sein d’images cliniques de patients présentant des pathologies rachidiennes, et ce dans le disque intervertébral (DIV) complet, dans l’annulus fibrosus (AF) et dans le nucleus pulposus (NP). Ces outils permettront de vérifier si les dégénérescences des DIV sont spécifiques aux pathologies les ayant engendrées ainsi qu’à leurs sévérités. Afin d’analyser le signal IRM tridimensionnel du DIV de manière automatisée, il est nécessaire de segmenter les images cliniques. Une méthode semi-automatisée a été utilisée dans ce projet et sa reproductibilité a été testée. Cette méthode permet un gain de temps par rapport aux méthodes manuelles utilisées dans la littérature. Des outils d’analyse de signal IRM au sein du DIV ont été développés afin de détecter la sensibilité de celui-ci aux pathologies rachidiennes et à leurs sévérités. Ces outils ont permis de refléter les variations de distribution du signal IRM de manière géométrique en 3D et de manière gaussienne. Dans ce but, une cohorte de 79 sujets (32 scolioses, 32 spondylolithésis, 15 contrôles) a été étudiée. Une normalisation de l’intensité de signal a été nécessaire à la comparaison des signaux----------ABSTRACT Scoliosis and spondylolisthesis are spine pathologies affecting 1.5-3% and 13.6% of the population, respectively. These diseases have the potential to further progress. These tridimensional pathologies are mainly studied using two-dimensional geometric indices, which reflect only a fraction of the morphological, biomechanical and biochemical variations of the spine. Clinical interpretations of these pathologies and of their evolution are based on the limited information of spine modifications. The intervertebral disc (IVD) is the soft tissue between adjacent vertebrae that allow the mobility of the spine between the rigid segments. Spine pathologies lead to premature degeneration of the IVD. In our study, we hypothesize that the MRI signal intensity within clinical T2-weighted images is sensitive to the spine pathology and to its severity. Thus this imaging technique could provide information on the geometrical, biochemical and mechanical properties of the IVD, and facilitate in-vivo follow-up of the evolution of these spine pathologies. This project aims to develop techniques to analyse the tridimensional geometry as well as the Gaussian distribution of the T2- weighted MRI signal within clinical images of the IVD, the annulus fibrosus (AF) and the nucleus pulposus (NP) of patients affected with various spine pathologies. These tools will assess whether or not a specific degeneration of the IVD is caused by the spine pathologies depending on their severity. In order to analyse automatically the tridimensional MRI signal within the IVD, it is necessary to segment clinical images. A semi-automated method was used in this project and its reproducibility was tested. This method is less time-consuming compared to the commonly used manual methods that are reported in the literature. MRI signal analysis tools were developed to detect its sensitivity within the IVD to spine pathologies and their severities. These tools allowed a Gaussian and geometric distribution analysis of the MRI signal intensity within the IVD. A cohort of 79 subjects (32 scoliosis, 32 spondylolisthesis, 15 controls) was studied. A normalization of the signal intensity was done in order to compare images from patients with variable parameters such as the acquisition gain. This study tested two normalizations of the intensity of the signal. The first one was based on the intensity within the cerebrospinal flui

Deformable Multisurface Segmentation of the Spine for Orthopedic Surgery Planning and Simulation

Purpose: We describe a shape-aware multisurface simplex deformable model for the segmentation of healthy as well as pathological lumbar spine in medical image data. Approach: This model provides an accurate and robust segmentation scheme for the identification of intervertebral disc pathologies to enable the minimally supervised planning and patient-specific simulation of spine surgery, in a manner that combines multisurface and shape statistics-based variants of the deformable simplex model. Statistical shape variation within the dataset has been captured by application of principal component analysis and incorporated during the segmentation process to refine results. In the case where shape statistics hinder detection of the pathological region, user assistance is allowed to disable the prior shape influence during deformation. Results: Results demonstrate validation against user-assisted expert segmentation, showing excellent boundary agreement and prevention of spatial overlap between neighboring surfaces. This section also plots the characteristics of the statistical shape model, such as compactness, generalizability and specificity, as a function of the number of modes used to represent the family of shapes. Final results demonstrate a proof-of-concept deformation application based on the open-source surgery simulation Simulation Open Framework Architecture toolkit. Conclusions: To summarize, we present a deformable multisurface model that embeds a shape statistics force, with applications to surgery planning and simulation