73 research outputs found

    Vertebral Compression Fracture Detection With Novel 3D Localisation

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    Vertebral compression fractures (VCF) often go undetected in radiology images, potentially leading to secondary fractures and permanent disability or even death. The objective of this thesis is to develop a fully automated method for detecting VCF in incidental CT images acquired for other purposes, thereby facilitating better follow up and treatment. The proposed approach is based on 3D localisation in CT images, followed by VCF detection in the localised regions. The 3D localisation algorithm combines deep reinforcement learning (DRL) with imitation learning (IL) to extract thoracic / lumbar spine regions from chest / abdomen CT scans. The algorithm generates six bounding boxes as Regions of Interest (ROI) using three different CNN models, with an average Jaccard Index (JI)/Dice Coefficient (DC) of 74.21%/84.71%. The extracted ROI were then divided into slices and the slices into patches to train four convolutional neural network (CNN) models for VCF detection at the patch level. The predictions from the patches were aggregated at bounding box level, and majority voting performed to decide on the presence / absence of VCF for a patient. The best performing model was a six layered CNN, which together with majority voting achieved threefold cross validation accuracy / F1 Score of 85.95% / 85.94% from 308 chest scans. The same model also achieved a fivefold cross validation accuracy / F1 score of 86.67% / 87.04% from 168 abdomen scans. Because of the success of the 3D localisation algorithm, it was also trained on other abdominal organs, namely the spleen and left and right kidneys, with promising results. The 3D localisation algorithm was enhanced to work with fused bounding boxes and also in semi-supervised mode to address the problem of annotation time by radiologists. Experiments using three different proportions of labelled and unlabelled data achieved fairly good performance, although not as good as the fully supervised equivalents. Finally, VCF detection in a weakly supervised multiple instance learning (MIL) setting was performed to reduce radiologists’ time for annotations, together with majority voting on the six bounding boxes. The best performing model was the six layered CNN which achieved threefold cross validation accuracy / F1 score of 81.05% / 80.74 % on 308 thoracic scans, and fivefold cross validation accuracy / F1 Score of 85.45% / 86.61% on 168 abdomen scans. Overall, the results are comparable to the state-of the art that used an order of magnitude more scans

    Experimental biomechanics of vertebral fractures

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    Vertebral fractures are a severe cause of morbidity and disability. In particular, burst fractures are a common traumatic injury presenting neurological impairment in 47 % of cases. However, diagnosis and planning of the treatment is challenging as the injury originates in highly dynamic conditions. Short-segment pedicle instrumentation (SSPI) in combination with kyphoplasty (SSPI–KP) has been used to provide additional stabilisation of the fracture. However, there is a lack of understanding about the effectiveness SSPI–KP. The aim of this study was to follow the fracture pathway, from onset to the outcome of surgical treatment. The first part focused on the phenomena underlying fracture creation and the dynamics of interpedicular widening (IPW). Although associated with neurological deficit, no previous study has shown how IPW evolves at fracture initiation. Subsequently the performance of treatment was assessed to evaluate how KP can improve SSPI to a simulated early follow-up. Burst fractures were induced in 12 human three-adjacent-vertebrae segments. Following fracture investigation, SSPI and SSPI–KP were performed, and samples underwent fatigue loading. Image processing of high-resolution CT scans was performed to assess anatomical changes at consecutive experimental stages on the treated and adjacent vertebrae. Experiments proved that IPW reaches a maximum at fracture onset and then decreases to the value measured clinically. SSPI–KP marginally improved stability of the treated spine, whilst providing a significant restoration of the endplate geometry. Vertebral body underwent significant changes in height and endplate curvature throughout the fracture pathway. This study provided further insight on the biomechanics of vertebral fractures and the findings can be used to improve and/or develop novel treatments as well as validate numerical models for retrospective assessment of the injury. In addition, outcomes from the collaboration work on the development of a computational simulation may help better understand cancer related vertebral fractures

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

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    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

    Current and emerging artificial intelligence applications for pediatric musculoskeletal radiology

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    Artificial intelligence (AI) is playing an ever-increasing role in radiology (more so in the adult world than in pediatrics), to the extent that there are unfounded fears it will completely take over the role of the radiologist. In relation to musculoskeletal applications of AI in pediatric radiology, we are far from the time when AI will replace radiologists; even for the commonest application (bone age assessment), AI is more often employed in an AI-assist mode rather than an AI-replace or AI-extend mode. AI for bone age assessment has been in clinical use for more than a decade and is the area in which most research has been conducted. Most other potential indications in children (such as appendicular and vertebral fracture detection) remain largely in the research domain. This article reviews the areas in which AI is most prominent in relation to the pediatric musculoskeletal system, briefly summarizing the current literature and highlighting areas for future research. Pediatric radiologists are encouraged to participate as members of the research teams conducting pediatric radiology artificial intelligence research

    Body Mass Estimation from the Human Skeleton

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    The established methods for estimating average body mass from the skeleton are of two types: biomechanical and morphometric. Neither technique currently addresses the extremes of body mass (e.g. emaciation or obesity). The goal of this research is to explore several different biomechanical methods, using data collected from high resolution computed tomographic scans and macroscopic analysis of 150 known modern individuals from the William M. Bass Donated Skeleton Collection at the University of Tennessee, Knoxville. This research will review the biomechanics of human gait and the biomechanical accommodations that occur with increased obesity and load bearing. The analysis will include cross-sectional geometry of the human femur at five locations along the diaphysis, bone mineral density scans of the proximal femur and a macroscopic evaluation of degenerative changes of the articulations of the spine, hip, knee and foot. The best single indicator of body mass for both males and females is the cross-sectional area of the proximal femur and BMD. By using pathologies combined, an accuracy rate of 87% for predicting obesity was achieved using a classification tree with sexes pooled. Furthermore, severe obesity has such a profound effect on the human skeleton as to leave a suite of traits affecting the load bearing elements of the lower limb and vertebral column
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