726 research outputs found

    Three Dimensional Nonlinear Statistical Modeling Framework for Morphological Analysis

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    This dissertation describes a novel three-dimensional (3D) morphometric analysis framework for building statistical shape models and identifying shape differences between populations. This research generalizes the use of anatomical atlases on more complex anatomy as in case of irregular, flat bones, and bones with deformity and irregular bone growth. The foundations for this framework are: 1) Anatomical atlases which allow the creation of homologues anatomical models across populations; 2) Statistical representation for output models in a compact form to capture both local and global shape variation across populations; 3) Shape Analysis using automated 3D landmarking and surface matching. The proposed framework has various applications in clinical, forensic and physical anthropology fields. Extensive research has been published in peer-reviewed image processing, forensic anthropology, physical anthropology, biomedical engineering, and clinical orthopedics conferences and journals. The forthcoming discussion of existing methods for morphometric analysis, including manual and semi-automatic methods, addresses the need for automation of morphometric analysis and statistical atlases. Explanations of these existing methods for the construction of statistical shape models, including benefits and limitations of each method, provide evidence of the necessity for such a novel algorithm. A novel approach was taken to achieve accurate point correspondence in case of irregular and deformed anatomy. This was achieved using a scale space approach to detect prominent scale invariant features. These features were then matched and registered using a novel multi-scale method, utilizing both coordinate data as well as shape descriptors, followed by an overall surface deformation using a new constrained free-form deformation. Applications of output statistical atlases are discussed, including forensic applications for the skull sexing, as well as physical anthropology applications, such as asymmetry in clavicles. Clinical applications in pelvis reconstruction and studying of lumbar kinematics and studying thickness of bone and soft tissue are also discussed

    A shape analysis approach to prediction of bone stiffness using FEXI

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    The preferred method of assessing the risk of an osteoporosis related fracture is currently a measure of bone mineral density (BMD) by dual energy X-ray absorptiometry (DXA). However, other factors contribute to the overall risk of fracture, including anatomical geometry and the spatial distribution of bone. Finite element analysis can be performed in both two and three dimensions, and predicts the deformation or induced stress when a load is applied to a structure (such as a bone) of defined material composition and shape. The simulation of a mechanical compression test provides a measure of whole bone stiffness (N mm−1). A simulation system was developed to study the sensitivity of BMD, 3D and 2D finite element analysis to variations in geometric parameters of a virtual proximal femur model. This study demonstrated that 3D FE and 2D FE (FEXI) were significantly more sensitive to the anatomical shape and composition of the proximal femur than conventional BMD. The simulation approach helped to analyse and understand how variations in geometric parameters affect the stiffness and hence strength of a bone susceptible to osteoporotic fracture. Originally, the FEXI technique modelled the femur as a thin plate model of an assumed constant depth for finite element analysis (FEA). A better prediction of tissue depth across the bone, based on its geometry, was required to provide a more accurate model for FEA. A shape template was developed for the proximal femur to provide this information for the 3D FE analysis. Geometric morphometric techniques were used to procure and analyse shape information from a set of CT scans of excised human femora. Generalized Procrustes Analysis and Thin Plate Splines were employed to analyse the data and generate a shape template for the proximal femur. 2D Offset and Depth maps generated from the training set data were then combined to model the three-dimensional shape of the bone. The template was used to predict the three-dimensional bone shape from a 2D image of the proximal femur procured through a DXA scan. The error in the predicted 3D shape was measured as the difference in predicted and actual depths at each pixel. The mean error in predicted depths was found to be 1.7mm compared to an average bone depth of 34mm. 3D FEXI analysis on the predicted 3D bone along with 2D FEXI for a stance loading condition and BMD measurement were performed based on 2D radiographic projections of the CT scans and compared to bone stiffness results obtained from finite element analysis of the original 3D CT scans. 3D FEXI provided a significantly higher correlation (R2 = 0.85) with conventional CT derived 3D finite element analysis than achieved with both BMD (R2 = 0.52) and 2D FEXI (R2 = 0.44)

    Bone segmentation and 3D visualization of CT images for traumatic pelvic injuries

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    Pelvic bone segmentation is a vital step in analyzing pelvic CT images, which assists physicians with diagnostic decision making in cases of traumatic pelvic injuries. Due to the limited resolution of the original CT images and the complexity of pelvic structures and their possible fractures, automatic pelvic bone segmentation in multiple CT slices is very difficult. In this study, an automatic pelvic bone segmentation approach is proposed using the combination of anatomical knowledge and computational techniques. It is developed for solving the problem of accurate and efficient bone segmentation using multiple consecutive pelvic CT slices obtained from each patient. Our proposed segmentation method is able to handle variation of bone shapes between slices there by making it less susceptible to inter‐personal variability between different patients' data. Moreover, the designed training models are validated using a cross‐validation process to demonstrate the effectiveness. The algorithm's capability is tested on a set of 20 CT data sets. Successful segmentation results and quantitative evaluations are present to demonstrate the effectiveness and robustness of proposed algorithm, well suited for pelvic bone segmentation purposes.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106095/1/ima22076.pd

    Development and Implementation of a Computational Surgical Planning Model for Pre-Operative Planning and Post-Operative Assessment and Analysis of Total Hip Arthroplasty

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    Total hip arthroplasty (THA) is most often used to treat osteoarthritis of the hip joint. Due to lack of a better alternative, newer designs are evaluated experimentally using mechanical simulators and cadavers. These evaluation techniques, though necessary, are costly and time-consuming, limiting testing on a broader population. Due to the advancement in technology, the current focus has been to develop patient-specific solutions. The hip joint can be approximated as encompassing a bone socket geometry, and therefore the shapes of the implant are well constrained. The variability of performance after the surgery is mostly driven by surgical procedures. It is believed that placing the acetabular component within the “safe zone” will commonly lead to successful surgical outcomes [1]. Unfortunately, recent research has revealed problems with the safe zone concept, and there is a need for a better tool which can aid surgeons in planning for surgery.With the advancement of computational power, more recent focus has been applied to the development of simulation tools that can predict implant performances. In this endeavor, a virtual hip simulator is being developed at the University of Tennessee Knoxville to provide designers and surgeons alike instant feedback about the performance of the hip implants. The mathematical framework behind this tool has been developed.In this dissertation, the primary focus is to further expand the capabilities of the existing hip model and develop the front-end that can replicate a total hip arthroplasty surgery procedure pre-operatively, intra-operatively, and post-operatively. This new computer-assisted orthopaedic surgical tool will allow surgeons to simulate surgery, then predict, compare, and optimize post-operative THA outcomes based on component placement, sizing choices, reaming and cutting locations, and surgical methods. This more advanced mathematical model can also reveal more information pre-operatively, allowing a surgeon to gain ample information before surgery, especially with difficult and revision cases. Moreover, this tool could also help during the implant development design process as designers can instantly simulate the performance of their new designs, under various surgical, simulated in vivo conditions

    A total hip replacement toolbox : from CT-scan to patient-specific FE analysis

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    FACTS: Fully Automatic CT Segmentation of a Hip Joint

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    Extraction of surface models of a hip joint from CT data is a pre-requisite step for computer assisted diagnosis and planning (CADP) of periacetabular osteotomy (PAO). Most of existing CADP systems are based on manual segmentation, which is time-consuming and hard to achieve reproducible results. In this paper, we present a Fully Automatic CT Segmentation (FACTS) approach to simultaneously extract both pelvic and femoral models. Our approach works by combining fast random forest (RF) regression based landmark detection, multi-atlas based segmentation, with articulated statistical shape model (aSSM) based fitting. The two fundamental contributions of our approach are: (1) an improved fast Gaussian transform (IFGT) is used within the RF regression framework for a fast and accurate landmark detection, which then allows for a fully automatic initialization of the multi-atlas based segmentation; and (2) aSSM based fitting is used to preserve hip joint structure and to avoid penetration between the pelvic and femoral models. Taking manual segmentation as the ground truth, we evaluated the present approach on 30 hip CT images (60 hips) with a 6-fold cross validation. When the present approach was compared to manual segmentation, a mean segmentation accuracy of 0.40, 0.36, and 0.36 mm was found for the pelvis, the left proximal femur, and the right proximal femur, respectively. When the models derived from both segmentations were used to compute the PAO diagnosis parameters, a difference of 2.0 ± 1.5°, 2.1 ± 1.6°, and 3.5 ± 2.3% were found for anteversion, inclination, and acetabular coverage, respectively. The achieved accuracy is regarded as clinically accurate enough for our target applications

    Analysis, Segmentation and Prediction of Knee Cartilage using Statistical Shape Models

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    Osteoarthritis (OA) of the knee is one of the leading causes of chronic disability (along with the hip). Due to rising healthcare costs associated with OA, it is important to fully understand the disease and how it progresses in the knee. One symptom of knee OA is the degeneration of cartilage in the articulating knee. The cartilage pad plays a major role in painting the biomechanical picture of the knee. This work attempts to quantify the cartilage thickness of healthy male and female knees using statistical shape models (SSMs) for a deep knee bend activity. Additionally, novel cartilage segmentation from magnetic resonance imaging (MRI) and estimation algorithms from computer tomography (CT) or x-rays are proposed to facilitate the efficient development and accurate analysis of future treatments related to the knee. Cartilage morphology results suggest distinct patterns of wear in varus, valgus, and neutral degenerative knees, and examination of contact regions during the deep knee bend activity further emphasizes these patterns. Segmentation results were achieved that were comparable if not of higher quality than existing state-of-the-art techniques for both femoral and tibial cartilage. Likewise, using the point correspondence properties of SSMs, estimation of articulating cartilage was effective in healthy and degenerative knees. In conclusion, this work provides novel, clinically relevant morphological data to compute segmentation and estimate new data in such a way to potentially contribute to improving results and efficiency in evaluation of the femorotibial cartilage layer

    Automated motion analysis of bony joint structures from dynamic computer tomography images: A multi-atlas approach

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    Dynamic computer tomography (CT) is an emerging modality to analyze in-vivo joint kinematics at the bone level, but it requires manual bone segmentation and, in some instances, landmark identification. The objective of this study is to present an automated workflow for the assessment of three-dimensional in vivo joint kinematics from dynamic musculoskeletal CT images. The proposed method relies on a multi-atlas, multi-label segmentation and landmark propagation framework to extract bony structures and detect anatomical landmarks on the CT dataset. The segmented structures serve as regions of interest for the subsequent motion estimation across the dynamic sequence. The landmarks are propagated across the dynamic sequence for the construction of bone embedded reference frames from which kinematic parameters are estimated. We applied our workflow on dynamic CT images obtained from 15 healthy subjects on two different joints: thumb base (n = 5) and knee (n = 10). The proposed method resulted in segmentation accuracies of 0.90 ± 0.01 for the thumb dataset and 0.94 ± 0.02 for the knee as measured by the Dice score coefficient. In terms of motion estimation, mean differences in cardan angles between the automated algorithm and manual segmentation, and landmark identification performed by an expert were below 1◦. Intraclass correlation (ICC) between cardan angles from the algorithm and results from expert manual landmarks ranged from 0.72 to 0.99 for all joints across all axes. The proposed automated method resulted in reproducible and reliable measurements, enabling the assessment of joint kinematics using 4DCT in clinical routine

    Towards a framework for multi class statistical modelling of shape, intensity, and kinematics in medical images

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    Statistical modelling has become a ubiquitous tool for analysing of morphological variation of bone structures in medical images. For radiological images, the shape, relative pose between the bone structures and the intensity distribution are key features often modelled separately. A wide range of research has reported methods that incorporate these features as priors for machine learning purposes. Statistical shape, appearance (intensity profile in images) and pose models are popular priors to explain variability across a sample population of rigid structures. However, a principled and robust way to combine shape, pose and intensity features has been elusive for four main reasons: 1) heterogeneity of the data (data with linear and non-linear natural variation across features); 2) sub-optimal representation of three-dimensional Euclidean motion; 3) artificial discretization of the models; and 4) lack of an efficient transfer learning process to project observations into the latent space. This work proposes a novel statistical modelling framework for multiple bone structures. The framework provides a latent space embedding shape, pose and intensity in a continuous domain allowing for new approaches to skeletal joint analysis from medical images. First, a robust registration method for multi-volumetric shapes is described. Both sampling and parametric based registration algorithms are proposed, which allow the establishment of dense correspondence across volumetric shapes (such as tetrahedral meshes) while preserving the spatial relationship between them. Next, the framework for developing statistical shape-kinematics models from in-correspondence multi-volumetric shapes embedding image intensity distribution, is presented. The framework incorporates principal geodesic analysis and a non-linear metric for modelling the spatial orientation of the structures. More importantly, as all the features are in a joint statistical space and in a continuous domain; this permits on-demand marginalisation to a region or feature of interest without training separate models. Thereafter, an automated prediction of the structures in images is facilitated by a model-fitting method leveraging the models as priors in a Markov chain Monte Carlo approach. The framework is validated using controlled experimental data and the results demonstrate superior performance in comparison with state-of-the-art methods. Finally, the application of the framework for analysing computed tomography images is presented. The analyses include estimation of shape, kinematic and intensity profiles of bone structures in the shoulder and hip joints. For both these datasets, the framework is demonstrated for segmentation, registration and reconstruction, including the recovery of patient-specific intensity profile. The presented framework realises a new paradigm in modelling multi-object shape structures, allowing for probabilistic modelling of not only shape, but also relative pose and intensity as well as the correlations that exist between them. Future work will aim to optimise the framework for clinical use in medical image analysis
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