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

Multi-Surface Simplex Spine Segmentation for Spine Surgery Simulation and Planning

This research proposes to develop a knowledge-based multi-surface simplex deformable model for segmentation of healthy as well as pathological lumbar spine data. It aims to provide a more accurate and robust segmentation scheme for identification of intervertebral disc pathologies to assist with spine surgery planning. A robust technique that combines multi-surface and shape statistics-aware variants of the deformable simplex model is presented. 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 have been validated against user-assisted expert segmentation

Lumbar disk 3D modeling from limited number of MRI axial slices

This paper studies the problem of clinical MRI analysis in the field of lumbar intervertebral disk herniation diagnosis. It discusses the possibility of assisting radiologists in reading the patients MRI images by constructing a 3D model for the region of interest using simple computer vision methods. We use axial MRI slices of the lumbar area. The proposed framework works with a very small number of MRI slices and goes through three main stages. Namely, the region of interest extraction and enhancement, inter-slice interpolation, and 3D model construction. We use the Marching Cubes algorithm to construct the 3D model of the the region of interest. The validation of our 3D models is based on a radiologist’s analysis of the models. We tested the proposed 3D model construction on 83 cases and We have a 95% accuracy according to the radiologist evaluation. This study shows that 3D model construction can greatly ease the task of the radiologist which enhances the working experience. This leads eventually to more accurate and easy diagnosis process

Improving radiologists’ and orthopedists’ QoE in diagnosing lumbar disk herniation using 3D modeling

This article studies and analyzes the use of 3D models, built from magnetic resonance imaging (MRI) axial scans of the lumbar intervertebral disk, that are needed for the diagnosis of disk herniation. We study the possibility of assisting radiologists and orthopedists and increasing their quality of experience (QoE) during the diagnosis process. The main aim is to build a 3D model for the desired area of interest and ask the specialists to consider the 3D models in the diagnosis process instead of considering multiple axial MRI scans. We further propose an automated framework to diagnose the lumber disk herniation using the constructed 3D models. We evaluate the effectiveness of increasing the specialists QoE by conducting a questionnaire on 14 specialists with different experiences ranging from residents to consultants. We then evaluate the effectiveness of the automated diagnosis framework by training it with a set of 83 cases and then testing it on an unseen test set. The results show that the the use of 3D models increases doctors QoE and the automated framework gets 90% of diagnosis accuracy

Segmentação de discos intervertebrais lombares para modelação e simulação computacional

Tese de Mestrado Integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica), 2022, Universidade de Lisboa, Faculdade de CiênciasA lombalgia é a principal causa de incapacidade a nível mundial. A degeneração do disco intervertebral é uma das causas da lombalgia, podendo em casos avançados necessitar da remoção do disco intervertebral e substituição deste por um implante. Este implante pode consistir num dispositivo contendo enxerto ósseo (fusão espinhal) ou num disco intervertebral artificial (artroplastia discal). Ambos os métodos apresentam vantagens e desvantagens, pelo que é importante estudar, através de modelação e simulação em elementos finitos, a forma como implantes específicos afetam a biomecânica da coluna lombar antes de os inserir. Esta modelação personalizada requer a capacidade de segmentar as estruturas anatómicas relevantes a partir de imagens médicas. O presente trabalho teve como principal objetivo a implementação/desenvolvimento de um método para localizar e segmentar automaticamente discos intervertebrais lombares em 3D a partir de imagens de ressonância magnética em ponderação T2, com o intuito de auxiliar a construção de modelos de elementos finitos da coluna lombar a partir de casos reais, fornecendo informação precisa e personalizada sobre a forma dos discos intervertebrais do paciente. O desenvolvimento do método para permitir adicionalmente segmentar separadamente as duas principais estruturas do disco – anel fibroso e núcleo pulposo – e detetar automaticamente casos em que a degeneração não permite fazer esta distinção foi posteriormente seguido como objetivo secundário. O método de segmentação foi desenvolvido a partir de um método pré-existente na literatura para realização de segmentações 2D no perfil sagital, tendo este sido parcialmente implementado, modificado e adaptado para uso em 3D. O método permitiu realizar segmentações com uma exatidão média de 87.0 ± 3.7% medida pelo coeficiente de Dice em relação a segmentações manuais de referência. Esta eficácia é comparável com outros métodos de segmentação 3D na literatura. Este método apresenta a vantagem de ser significativamente mais rápido que a maioria dos métodos existentes, demorando apenas alguns segundos para completar uma segmentação dos discos lombares. O método para detetar degeneração discal identificou corretamente o estado de 96% dos discos (saudáveis e degenerados) com que foi testado.Back pain, especially in the lumbar spine, is the main cause of disability in the world. Intervertebral disc (IVD) degeneration is one of the causes of back pain. In some cases this requires the removal of the disc and its replacement with an implant. This implant may consist of either a cage containing bone graft (spinal fusion) or an artificial IVD (disc arthroplasty). Both of these treatments have advantages and disadvantages, which is why it is important to study, through computer modeling and finite element simulation, the ways in which specific implants affect the biomechanics of the lumbar spine before inserting them. This customized modeling requires the ability to segment the relevant anatomical structures from medical images. The present work had as its main objective the implementation/development of a method for localizing and automatically segmenting lumbar IVDs in 3D from T2 weighted magnetic resonance imaging, with the goal of supporting and complementing the generation of finite element models from real lumbar spines, by providing accurate and personalized information on the shape of the patient’s IVDs. The development of the method to also allow performing separate segmentations of the IVD’s two main structures – annulus fibrosus and nucleus pulposus – as well as automatically detecting degenerated IVDs where this distinction is no longer possible was later pursued as a secondary objective. The segmentation method was developed from a pre-existing method in the literature aimed at performing 2D segmentations in the sagittal profile, which was partially implemented, modified and adapted to 3D use. The method performed segmentations with a mean accuracy of 87.0 ± 3.7% as measured by the Dice coefficient in relation to manually segmented reference volumes, or ground truths. This method has the advantage of being significantly faster than most existing 3D segmentation methods, requiring only a few seconds to perform a complete segmentation of the lumbar discs. The method for detecting IVD degeneration correctly identified the status of 96% of the discs (healthy and degenerated) on which it was tested

Analysis of uncertainty and variability in finite element computational models for biomedical engineering: characterization and propagation

Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering

Precision Medicine for the Intervertebral Disc: Image Processing and Computational Modeling of Disc Degeneration to Inform Patient-Specific Therapies

The human intervertebral disc is a deceptively simple structure which plays an essential role in human movement. Even slight changes to the disc microenvironment can have far reaching consequences, particularly when these changes result in disc degeneration and herniation. Degeneration is increasingly tied to lower back pain; better understanding this complex relationship could enable more informed treatments and better outcomes for patients suffering from low back pain. The overall aim of the work presented here is to investigate tools and techniques which might aid in patient evaluation, for use in both research and clinical settings. Specifically, this includes (1) development of a patient-specific finite element model of nutrient transport in the disc, and (2) quantitative comparison of a novel imaging modality (apparent diffusion coefficient mapping) to established modalities for disc visualization. In the first investigation, results of the patient-specific model showed distinct diffusion behavior between patients, even within discs of the same degeneration grade, indicating the importance of the patient-specific diffusivities to the accurate prediction of nutrient availability in the disc. In the second investigation, the results indicated a general sensitivity of both modalities to degeneration of the NP. However, significant differences were found between the modalities at measuring the same features, indicating that the two modalities have fundamentally different capabilities for IVD visualization and implying that one is more accurate than the other. Validating this will be the focus of future work

Statistical anatomical modelling for efficient and personalised spine biomechanical models

Personalised medicine is redefining the present and future of healthcare by increasing treatment efficacy and predicting diseases before they actually manifest. This innovative approach takes into consideration patient’s unique genes, environment, and lifestyle. An essential component is physics-based simulations, which allows the outcome of a treatment or a disease to be replicated and visualised using a computer. The main requirement to perform this type of simulation is to build patient-specific models. These models require the extraction of realistic object geometries from images, as well as the detection of diseases or deformities to improve the estimation of the material properties of the studied object. The aim of this thesis was the design of a general framework for creating patient- specific models for biomechanical simulations using a framework based on statistical shape models. The proposed methodology was tested on the construction of spine models, including vertebrae and intervertebral discs (IVD). The proposed framework is divided into three well-defined components: The paramount and first step is the extraction of the organ or anatomical structure from medical images. In the case of the spine, IVDs and vertebrae were extracted from Magnetic Resonance images (MRI) and Computed Tomography (CT), respectively. The second step is the classification of objects according to different factors, for instance, bones by its type and grade of fracture or IVDs by its degree of degeneration. This process is essential to properly model material properties, which depends on the possible pathologies of the tissue. The last component of the framework is the creation of the patient-specific model itself by combining the information from previous steps. The behaviour of the developed algorithms was tested using different datasets of spine images from both computed tomography (CT) and Magnetic resonance (MR) images from different institutions, type of population and image resolution

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