Fabric Image Representation Encoding Networks for Large-scale 3D Medical Image Analysis

Deep neural networks are parameterised by weights that encode feature representations, whose performance is dictated through generalisation by using large-scale feature-rich datasets. The lack of large-scale labelled 3D medical imaging datasets restrict constructing such generalised networks. In this work, a novel 3D segmentation network, Fabric Image Representation Networks (FIRENet), is proposed to extract and encode generalisable feature representations from multiple medical image datasets in a large-scale manner. FIRENet learns image specific feature representations by way of 3D fabric network architecture that contains exponential number of sub-architectures to handle various protocols and coverage of anatomical regions and structures. The fabric network uses Atrous Spatial Pyramid Pooling (ASPP) extended to 3D to extract local and image-level features at a fine selection of scales. The fabric is constructed with weighted edges allowing the learnt features to dynamically adapt to the training data at an architecture level. Conditional padding modules, which are integrated into the network to reinsert voxels discarded by feature pooling, allow the network to inherently process different-size images at their original resolutions. FIRENet was trained for feature learning via automated semantic segmentation of pelvic structures and obtained a state-of-the-art median DSC score of 0.867. FIRENet was also simultaneously trained on MR (Magnatic Resonance) images acquired from 3D examinations of musculoskeletal elements in the (hip, knee, shoulder) joints and a public OAI knee dataset to perform automated segmentation of bone across anatomy. Transfer learning was used to show that the features learnt through the pelvic segmentation helped achieve improved mean DSC scores of 0.962, 0.963, 0.945 and 0.986 for automated segmentation of bone across datasets.Comment: 12 pages, 10 figure

Image-based biomechanical models of the musculoskeletal system

Finite element modeling is a precious tool for the investigation of the biomechanics of the musculoskeletal system. A key element for the development of anatomically accurate, state-of-the art finite element models is medical imaging. Indeed, the workflow for the generation of a finite element model includes steps which require the availability of medical images of the subject of interest: segmentation, which is the assignment of each voxel of the images to a specific material such as bone and cartilage, allowing for a three-dimensional reconstruction of the anatomy; meshing, which is the creation of the computational mesh necessary for the approximation of the equations describing the physics of the problem; assignment of the material properties to the various parts of the model, which can be estimated for example from quantitative computed tomography for the bone tissue and with other techniques (elastography, T1rho, and T2 mapping from magnetic resonance imaging) for soft tissues. This paper presents a brief overview of the techniques used for image segmentation, meshing, and assessing the mechanical properties of biological tissues, with focus on finite element models of the musculoskeletal system. Both consolidated methods and recent advances such as those based on artificial intelligence are described

Combined musculoskeletal and finite element modelling of the lumbar spine and lower limbs

Bone health deterioration is a major public health issue increasing the risk of fragility fracture with a substantial associated psychosocioeconomic impact. In the lumbar spine, physical deconditioning associated with ageing and chronic pain is a potential promoter of bone structural degradation. General guidelines for the limitation of bone loss and the management of pain have been issued, prescribing a healthy lifestyle and a minimum level of physical activity. However, there is no specific recommendation regarding targeted activities that can effectively maintain lumbar spine bone health in populations at risk. The aim of this thesis was to develop a new predictive computational modelling framework for the study of bone structural adaptation to healthy and pathological conditions in the lumbar spine. The approach is based on the combination of a musculoskeletal model of the lumbar spine and lower limbs with structural finite element models of the lumbar vertebrae. These models are built with bone and muscle geometries derived from healthy individuals. Based on daily living activities, musculoskeletal simulations provide physiological loading conditions to the finite element models. Cortical and trabecular bone are modelled with shell and truss elements whose thicknesses and radii are adapted to withstand the physiological mechanical environment using a strain driven optimisation algorithm. This modelling framework allows to generate healthy bone architecture when a loading envelope representative of a healthy lifestyle is applied to the vertebrae, and identify influential activities. Prediction of bone remodelling under altered loading scenarios characteristic of lumbar pathologies can also be achieved. The modelling approach developed in this thesis is a powerful tool for the investigation of bone remodelling in the lumbar spine. Preliminary results indicate that locomotion activities are insufficient to maintain lumbar spine bone health. Specific recommendations to limit the effect of physical deconditioning related to muscle weakening back pain are suggested. The approach is also promising for the investigation of other lumbar pathologies such as age related osteoporosis and scoliosis.Open Acces

Book of Abstracts 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization

In this edition, the two events will run together as a single conference, highlighting the strong connection with the Taylor & Francis journals: Computer Methods in Biomechanics and Biomedical Engineering (John Middleton and Christopher Jacobs, Eds.) and Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (JoãoManuel R.S. Tavares, Ed.). The conference has become a major international meeting on computational biomechanics, imaging andvisualization. In this edition, the main program includes 212 presentations. In addition, sixteen renowned researchers will give plenary keynotes, addressing current challenges in computational biomechanics and biomedical imaging. In Lisbon, for the first time, a session dedicated to award the winner of the Best Paper in CMBBE Journal will take place. We believe that CMBBE2018 will have a strong impact on the development of computational biomechanics and biomedical imaging and visualization, identifying emerging areas of research and promoting the collaboration and networking between participants. This impact is evidenced through the well-known research groups, commercial companies and scientific organizations, who continue to support and sponsor the CMBBE meeting series. In fact, the conference is enriched with five workshops on specific scientific topics and commercial software.info:eu-repo/semantics/draf

OpenSim-Based Musculoskeletal Modeling: Foundation for Interactive Obstetric Simulator

The use of mathematical and computational models to understand complex biological systems, such as the human birth process, is a rapidly growing field in medicine. These models can be used to optimize and personalize medical treatments for individual patients, enhance training, and aid in educational efforts. While recent advancements in healthcare, particularly in obstetrics, have improved care for mothers and babies, studies and government reports indicate a rising rate of maternal mortality in the United States. Despite this rising trend, there is a lack of detailed studies concerning the use of modeling and simulation to develop an interactive obstetrics simulator that can aid both practitioners and patients. This research builds upon a novel template for developing an interactive obstetric simulator and aims to replicate an onerous finite element vaginal delivery simulation with an interactive, patient-specific simulator that emphasizes musculoskeletal dynamics. The study utilizes the open-source platform of OpenSim and inverse-kinematic solutions to develop fetal and maternal musculoskeletal models and simulate birth on the musculoskeletal level

Adaptation of the proximal humerus to physical activity: a within-subject controlled study in baseball players

The proximal humerus is a common, yet understudied site for osteoporotic fracture. The current study explored the impact of prolonged physical activity on proximal humerus bone health by comparing bone properties between the throwing and nonthrowing arms within professional baseball players. The proximal humerus in throwing arms had 28.1% (95% CI, 17.8 to 38.3%) greater bone mass compared to nonthrowing arms, as assessed using dual-energy x-ray absorptiometry. At the level of the surgical neck, computed tomography revealed 12.0% (95% CI, 8.2 to 15.8%) greater total cross-sectional area and 31.0% (95% CI, 17.8 to 44.2%) greater cortical thickness within throwing arms, which contributed to 56.8% (95% CI, 44.9 to 68.8%) greater polar moment of inertia (i.e., estimated ability to resist torsional forces) compared to nonthrowing arms. Within the humeral head and greater tubercle regions, throwing arms had 3.1% (95% CI, 1.1 to 5.1%) more trabecular bone, as assessed using high-resolution magnetic resonance imaging. Three-dimensional mapping of voxel- and vertex-wise differences between arms using statistical parametric mapping techniques revealed throwing arms had adaptation within much of the proximal diaphysis, especially the posterolateral cortex. The pattern of proximal diaphysis adaptation approximated the pattern of strain energy distribution within the proximal humerus during a fastball pitch derived from a musculoskeletal and finite element model in a representative player. These data demonstrate the adaptive ability of the proximal humerus to physical activity-related mechanical loads. It remains to be established how they translate to exercise prescription to improve bone health within the proximal humerus, however, they provide unique insight into the relationship between prolonged loading and skeletal adaptation at a clinically relevant osteoporotic site

Development of ultrasound to measure deformation of functional spinal units in cervical spine

Neck pain is a pervasive problem in the general population, especially in those working in vibrating environments, e.g. military troops and truck drivers. Previous studies showed neck pain was strongly associated with the degeneration of intervertebral disc, which is commonly caused by repetitive loading in the work place. Currently, there is no existing method to measure the in-vivo displacement and loading condition of cervical spine on the site. Therefore, there is little knowledge about the alternation of cervical spine functionality and biomechanics in dynamic environments. In this thesis, a portable ultrasound system was explored as a tool to measure the vertebral motion and functional spinal unit deformation. It is hypothesized that the time sequences of ultrasound imaging signals can be used to characterize the deformation of cervical spine functional spinal units in response to applied displacements and loading. Specifically, a multi-frame tracking algorithm is developed to measure the dynamic movement of vertebrae, which is validated in ex-vivo models. The planar kinematics of the functional spinal units is derived from a dual ultrasound system, which applies two ultrasound systems to image C-spine anteriorly and posteriorly. The kinematics is reconstructed from the results of the multi-frame movement tracking algorithm and a method to co-register ultrasound vertebrae images to MRI scan. Using the dual ultrasound, it is shown that the dynamic deformation of functional spinal unit is affected by the biomechanics properties of intervertebral disc ex-vivo and different applied loading in activities in-vivo. It is concluded that ultrasound is capable of measuring functional spinal units motion, which allows rapid in-vivo evaluation of C-spine in dynamic environments where X-Ray, CT or MRI cannot be used.2020-02-20T00:00:00