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

Automated Distinct Bone Segmentation from Computed Tomography Images using Deep Learning

Large-scale CT scans are frequently performed for forensic and diagnostic purposes, to plan and direct surgical procedures, and to track the development of bone-related diseases. This often involves radiologists who have to annotate bones manually or in a semi-automatic way, which is a time consuming task. Their annotation workload can be reduced by automated segmentation and detection of individual bones. This automation of distinct bone segmentation not only has the potential to accelerate current workflows but also opens up new possibilities for processing and presenting medical data for planning, navigation, and education. In this thesis, we explored the use of deep learning for automating the segmentation of all individual bones within an upper-body CT scan. To do so, we had to find a network architec- ture that provides a good trade-off between the problem’s high computational demands and the results’ accuracy. After finding a baseline method and having enlarged the dataset, we set out to eliminate the most prevalent types of error. To do so, we introduced an novel method called binary-prediction-enhanced multi-class (BEM) inference, separating the task into two: Distin- guishing bone from non-bone is conducted separately from identifying the individual bones. Both predictions are then merged, which leads to superior results. Another type of error is tack- led by our developed architecture, the Sneaky-Net, which receives additional inputs with larger fields of view but at a smaller resolution. We can thus sneak more extensive areas of the input into the network while keeping the growth of additional pixels in check. Overall, we present a deep-learning-based method that reliably segments most of the over one hundred distinct bones present in upper-body CT scans in an end-to-end trained matter quickly enough to be used in interactive software. Our algorithm has been included in our groups virtual reality medical image visualisation software SpectoVR with the plan to be used as one of the puzzle piece in surgical planning and navigation, as well as in the education of future doctors

Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming

Animal production (e.g., milk, meat, and eggs) provides valuable protein production for human beings and animals. However, animal production is facing several challenges worldwide such as environmental impacts and animal welfare/health concerns. In animal farming operations, accurate and efficient monitoring of animal information and behavior can help analyze the health and welfare status of animals and identify sick or abnormal individuals at an early stage to reduce economic losses and protect animal welfare. In recent years, there has been growing interest in animal welfare. At present, sensors, big data, machine learning, and artificial intelligence are used to improve management efficiency, reduce production costs, and enhance animal welfare. Although these technologies still have challenges and limitations, the application and exploration of these technologies in animal farms will greatly promote the intelligent management of farms. Therefore, this Special Issue will collect original papers with novel contributions based on technologies such as sensors, big data, machine learning, and artificial intelligence to study animal behavior monitoring and recognition, environmental monitoring, health evaluation, etc., to promote intelligent and accurate animal farm management

KEER2022

Avanttítol: KEER2022. DiversitiesDescripció del recurs: 25 juliol 202

A Systematic Review and Meta-Analysis of the Incidence of Injury in Professional Female Soccer

The epidemiology of injury in male professional football is well documented and has been used as a basis to monitor injury trends and implement injury prevention strategies. There are no systematic reviews that have investigated injury incidence in women’s professional football. Therefore, the extent of injury burden in women’s professional football remains unknown. PURPOSE: The primary aim of this study was to calculate an overall incidence rate of injury in senior female professional soccer. The secondary aims were to provide an incidence rate for training and match play. METHODS: PubMed, Discover, EBSCO, Embase and ScienceDirect electronic databases were searched from inception to September 2018. Two reviewers independently assessed study quality using the Strengthening the Reporting of Observational Studies in Epidemiology statement using a 22-item STROBE checklist. Seven prospective studies (n=1137 professional players) were combined in a pooled analysis of injury incidence using a mixed effects model. Heterogeneity was evaluated using the Cochrane Q statistic and I2. RESULTS: The epidemiological incidence proportion over one season was 0.62 (95% CI 0.59 - 0.64). Mean total incidence of injury was 3.15 (95% CI 1.54 - 4.75) injuries per 1000 hours. The mean incidence of injury during match play was 10.72 (95% CI 9.11 - 12.33) and during training was 2.21 (95% CI 0.96 - 3.45). Data analysis found a significant level of heterogeneity (total Incidence, X2 = 16.57 P < 0.05; I2 = 63.8%) and during subsequent sub group analyses in those studies reviewed (match incidence, X2 = 76.4 (d.f. = 7), P <0.05; I2 = 90.8%, training incidence, X2 = 16.97 (d.f. = 7), P < 0.05; I2 = 58.8%). Appraisal of the study methodologies revealed inconsistency in the use of injury terminology, data collection procedures and calculation of exposure by researchers. Such inconsistencies likely contribute to the large variance in the incidence and prevalence of injury reported. CONCLUSIONS: The estimated risk of sustaining at least one injury over one football season is 62%. Continued reporting of heterogeneous results in population samples limits meaningful comparison of studies. Standardising the criteria used to attribute injury and activity coupled with more accurate methods of calculating exposure will overcome such limitations