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

New methodologies and scenarios for evaluating tidal current energy potential

Transition towards a low carbon economy raises concerns of loss of security of supply with high penetrations of renewable generation displacing traditional fossil fuel based generation. While wind and wave resources are increasingly forecastable, they are stochastic in nature. The tidal current resource, although variable has the advantage of being deterministic and truly predictable. With the first Crown Estate leasing round complete for wave and tidal current energy, plans are in place to install 1000 MW of tidal capacity in the Pentland Firth and Orkney waters. The aim of the work presented in this thesis is to examine the role tidal current energy can realistically play in the future electricity mix. To achieve this objective it was first necessary to develop new methodologies to capture the temporal and spatial variability of tidal current dynamics over long timescales and identify metrics relevant in a tidal energy context. These methodologies were developed for project scale resource characterisation, and provided a basis for development of a national scale dataset. The creation of project and national scale tidal datasets capture spatial and temporal variability at a level beyond previous insight, as demonstrated in case studies of three important early stage tidal current energy development sites. The provision of a robust national scale dataset enabled the development of realistic scenarios for the growth of the tidal current energy sector in UK waters. Assessing the various scenarios proposed indicates that first-generation technology solutions have the potential to generate up to 31 TWh/yr (over 8% of 2009 UK electricity demand). However, only 14 TWh/yr can be sensibly generated after incorporating realistic economic and environmental limitations proposed in this study. The preceding development of methodologies, datasets and scenarios enabled statistical analysis of the matching characteristics of future tidal energy generation potential with the present UK electricity demand and trends of electricity usage. This analysis demonstrated that the UK tidal current energy resource is much more in phase than has previously been understood, highlighting the flaws in previous studies suggesting that a combined portfolio of sites around the UK can deliver firm power. As there is negligible firm production, base-load contribution is insignificant. However, the time-series generated from this analysis identifies the role tidal current energy can play in meeting future energy demand and offer significant benefit for the operation of the electricity system as part of an integrated portfolio

Attention and Pooling based Sigmoid Colon Segmentation in 3D CT images

Segmentation of the sigmoid colon is a crucial aspect of treating diverticulitis. It enables accurate identification and localisation of inflammation, which in turn helps healthcare professionals make informed decisions about the most appropriate treatment options. This research presents a novel deep learning architecture for segmenting the sigmoid colon from Computed Tomography (CT) images using a modified 3D U-Net architecture. Several variations of the 3D U-Net model with modified hyper-parameters were examined in this study. Pyramid pooling (PyP) and channel-spatial Squeeze and Excitation (csSE) were also used to improve the model performance. The networks were trained using manually annotated sigmoid colon. A five-fold cross-validation procedure was used on a test dataset to evaluate the network's performance. As indicated by the maximum Dice similarity coefficient (DSC) of 56.92+/-1.42%, the application of PyP and csSE techniques improves segmentation precision. We explored ensemble methods including averaging, weighted averaging, majority voting, and max ensemble. The results show that average and majority voting approaches with a threshold value of 0.5 and consistent weight distribution among the top three models produced comparable and optimal results with DSC of 88.11+/-3.52%. The results indicate that the application of a modified 3D U-Net architecture is effective for segmenting the sigmoid colon in Computed Tomography (CT) images. In addition, the study highlights the potential benefits of integrating ensemble methods to improve segmentation precision.Comment: 8 Pages, 6 figures, Accepted at IEEE DICTA 202

Arpc1b, a centrosomal protein, is both an activator and substrate of Aurora A

In addition to its function as an Arp2/3 complex subunit, Arp1cb interacts with and stimulates Aurora A at centrosomes, functioning in cell cycle progression

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-standard errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Discovery and analysis of iron export and iron import mechanisms of Bradyrhizobium japonicum and their roles in managing stress responses

Nutritional iron acquisition by bacteria is well described, but almost nothing is known about bacterial iron export. Here, we show that Bradyrhizobium japonicum MbfA (Blr7895) is an inner membrane protein expressed in cells specifically under high iron conditions. An mbfA deletion mutant is severely defective in iron export activity, contains >2-fold more intracellular iron than the parent strain, and displays an aberrant iron-dependent gene expression phenotype. The findings suggest that iron export plays an important role in bacterial iron homeostasis, and MbfA is responsible for the iron export activity of B. japonicum. The N-terminal Ferritin like domain (FLD) of MbfA is localized to the cytoplasmic side of the inner membrane and is required for export activity. Purified FLD is a dimer in solution implying that MbfA functions as a dimer. An mbfA mutant is sensitive to short term exposure to high levels iron or H2O2 but not when grown in elevated iron media, suggesting a stress response adaptation. The bfr gene encodes the iron storage protein bacterioferritin. An mbfA bfr double mutant showed a loss of stress adaptation, and had a severe growth phenotype in high iron media. The double mutant exhibits elevated intracellular iron content than the wild type, and displays aberrant gene expression even when grown in relatively low iron media. These results suggest that MbfA and Bfr work in concert to manage iron and oxidative stresses. In addition, the need for iron detoxification is not limited to extreme environments, but is also required for normal cellular function. B. japonicum cannot make siderophores for acquisition of iron in aerobic environments. The mechanism of iron uptake in the absence of xenosiderophores is unknown. Exploiting the synthetic lethal phenotype of the mbfA bfr double mutant, we identified suppressor strains that can grow in high iron concentrations. The suppressor strains harbor loss of function mutations in the feoAB operon, which is a ferrous iron transport system. Interestingly, FeoAB system is required for ferric iron utilization and is required for high affinity uptake of both ferric and ferrous iron by B. japonicum. feoB and feoA incited small, poorly developed, non-nitrogen fixing nodules on soybean plants suggesting the requirement of FeoAB system for establishment of symbiosis. A suppressor strain harboring a Glu-40 to Lys mutation in FeoA (feoAE40K ) has diminished but measurable iron uptake activity in free living cells. It elicited nitrogen fixing nodules on soybean but the bacteroids in the nodules displayed lower iron uptake activity compared to wildtype bacteroids. This strongly suggests that the FeoAB transport system is involved in iron uptake within symbiotic bacteroids. Thus our results indicate that B. japonicum employs a single iron transporter to adapt to diverse environmental conditions

Vertebral compression fracture detection using imitation learning, patch based convolutional neural networks and majority voting

Vertebral compression fractures often go clinically undetected and consequently untreated, resulting in severe secondary fractures due to osteoporosis, and potentially leading to permanent disability or even death. Automated detection of vertebral compression fractures (VCF) could assist in routine screening and followup of incidentally scanned patients, thereby mitigating secondary fractures later. A novel fully automated method for the detection of VCF in 3D computed tomography (CT) of the chest or abdomen is presented in this work. It starts with 3D localisation of thoracic and lumbar spine regions using deep reinforcement learning (DRL) and imitation learning (IL). Six different 3D bounding boxes are generated by the localisation step, achieving an average Jaccard Index (JI)/ Dice Coefficient (DC) of 74.21%/84.71%, and detection accuracy of 97.16 % using 3 different CNN architectures. The localised region is then split into 2D sagittal slices around the coronal centre. Each slice is further divided into patches, on which convolutional neural networks (CNNs) are trained to detect VCF. Four different CNN architectures, namely 3 layered, 6 layered and transfer learning (TL) using VGG16 and ResNet50, were experimented with. The best performing architecture turned out to be the 6 layered CNN. Aggregation is performed on the VCF detection in the 2D Patches extracted from individual bounding boxes, followed by majority voting to arrive at the final decision on the status of VCF for a given patient. An average three-fold cross validation accuracy of 85.95%, sensitivity of 88.10%, specificity of 84.20% and F1 score of 85.94% were achieved on chest images using 6 layered CNN on chest images from 308 patients. An average five-fold cross validation accuracy of 86.67%, sensitivity of 88.13%, specificity of 85.02% and F1 Score of 87.04% were achieved on abdomen images from 168 patients with the 6 layered CNN