Deep convolutional networks for automated detection of posterior-element fractures on spine CT

Injuries of the spine, and its posterior elements in particular, are a common occurrence in trauma patients, with potentially devastating consequences. Computer-aided detection (CADe) could assist in the detection and classification of spine fractures. Furthermore, CAD could help assess the stability and chronicity of fractures, as well as facilitate research into optimization of treatment paradigms. In this work, we apply deep convolutional networks (ConvNets) for the automated detection of posterior element fractures of the spine. First, the vertebra bodies of the spine with its posterior elements are segmented in spine CT using multi-atlas label fusion. Then, edge maps of the posterior elements are computed. These edge maps serve as candidate regions for predicting a set of probabilities for fractures along the image edges using ConvNets in a 2.5D fashion (three orthogonal patches in axial, coronal and sagittal planes). We explore three different methods for training the ConvNet using 2.5D patches along the edge maps of 'positive', i.e. fractured posterior-elements and 'negative', i.e. non-fractured elements. An experienced radiologist retrospectively marked the location of 55 displaced posterior-element fractures in 18 trauma patients. We randomly split the data into training and testing cases. In testing, we achieve an area-under-the-curve of 0.857. This corresponds to 71% or 81% sensitivities at 5 or 10 false-positives per patient, respectively. Analysis of our set of trauma patients demonstrates the feasibility of detecting posterior-element fractures in spine CT images using computer vision techniques such as deep convolutional networks.Comment: To be presented at SPIE Medical Imaging, 2016, San Dieg

Vertebral Compression Fracture Detection With Novel 3D Localisation

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

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

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

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

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

The role of FUBP3 in osteoporosis

Osteoporosis is a disease of low bone mineral density (BMD) and altered bone architecture leading to an increased risk of fracture. Worldwide, osteoporosis affects millions of people and causes an enormous and increasing healthcare burden. Reduced BMD is the major risk factor for fracture and is a highly heritable trait. Genome wide association studies (GWAS) have identifed the 9q34.11 locus containing Far Upstream Binding Protein 3 (FUBP3) as associated with BMD, fracture and height. FUBP3 is a DNA and RNA binding protein and transcriptional regulator of c-myc, but has no previously confrmed role in the skeleton. I hypothesised that FUBP3 is the causative gene underlying the association with BMD, fracture and height at the 9q34.11 locus and that FUBP3 is needed for normal bone development and maintenance. To test this hypothesis, I used bioinformatic tools to interrogate the 9q34.11 locus and prioritise FUBP3 for further study and studied the skeletal phenotype of an FUBP3 deficient mouse. Skeletal assessment included analysis of growth, bone mineral content and bone structure, strength, and cellular parameters. Analysis of the 9q34.11 locus confirmed that FUBP3 was the most likely causal gene underlying the association with BMD and was expressed in bone. Compared to wild type mice, FUBP3 defcient mice were short and demonstrated reduced bone mineral content, altered trabecular bone parameters and reduced strength, suggesting an important role of FUBP3 in both development and adult bone maintenance. Finally, I investigated the cellular phenotype underlying the skeletal changes seen with FUBP3 loss. These did not demonstrate a deficit in osteoblastic bone formation or osteoclastic bone resorption with FUBP3 deficiency. Nevertheless, the data presented in this thesis provide the first demonstration of a functional role of FUBP3 in the skeleton and confirm FUBP3 as the most likely causal gene at the 9q34.11 BMD, height and fracture GWAS locus.Open Acces