Learning Rigid Image Registration - Utilizing Convolutional Neural Networks for Medical Image Registration

Many traditional computer vision tasks, such as segmentation, have seen large step-changes in accuracy and/or speed with the application of Convolutional Neural Networks (CNNs). Image registration, the alignment of two or more images to a common space, is a fundamental step in many medical imaging workflows. In this paper we investigate whether these techniques can also bring tangible benefits to the registration task. We describe and evaluate the use of convolutional neural networks (CNNs) for both mono- and multi- modality registration and compare their performance to more traditional schemes, namely multi-scale, iterative registration. This paper also investigates incorporating inverse consistency of the learned spatial transformations to impose additional constraints on the network during training and investigate any benefit in accuracy during detection. The approaches are validated with a series of artificial mono-modal registration tasks utilizing T1-weighted MR brain i mages from the Open Access Series of Imaging Studies (OASIS) study and IXI brain development dataset and a series of real multi-modality registration tasks using T1-weighted and T2-weighted MR brain images from the 2015 Ischemia Stroke Lesion segmentation (ISLES) challenge. The results demonstrate that CNNs give excellent performance for both mono- and multi- modality head and neck registration compared to the baseline method with significantly fewer outliers and lower mean errors

The changing nature and definitions of industrial design and implications for prospective undergraduate students

There are currently a wide range of Higher Education Industrial Design courses available in the UK. In the present era, a wider breadth of narrative has developed within the subject, and as a result the content of industrial design educational offerings varies considerably. The paper assesses the industry view of Industrial Design as a discipline from the perspective of those employing university graduates. These views illustrate a change in the discipline, and this is considered in respect to current education practice. The choice of entry courses for the student wishing to embark on a career in the subject has also widened. It is argued that at present, the access to courses offers a haphazard informational stream to the potential applicant. An approach to developing an online facility to enable potential students to apply for the right course is discussed. It is suggested that a consistent and comparable platform of guidance is needed by which potential students can identify and match the course offering against their aptitudes and aspirations. A framework for such a system is proposed. Given that course choice will ultimately define the nature of their career opportunities it is argued that this would be a useful and productive asset

FASTR: Using Local Structure Tensors as a Similarity Metric

AbstractWe describe a novel structural image descriptor for image registration called the Fractionally Anisotropic Structural Tensor Representation (FASTR), calculated from the local structural tensor (LST). The metric has several characteristics that are advantageous for multi-modality registration, such as not depending on absolute voxel intensities, and being insensitive to slowly varying in- tensity inhomogeneities across the image. This latter property is very useful, since many imaging modalities suffer from such artefacts. Registration accuracy is tested on both computed tomography (CT) to cone-beam CT (CBCT) rigid registration, and CT to magnetic resonance (MR) rigid registration. The performance is compared with Mutual Information (MI) metric and the Self Similarity Context (SSC) descriptor. The results show that, for images with significant intensity inhomogeneity, FASTR produced more accurate results than MI, and faster results than SSC. The results suggest FASTR gives similar benefits in images with intensity inhomogeneity, but at a fraction of the computation and memory demand

Impact bias in student evaluations of higher education

In the context of higher education, this study examines the extent to which affective evaluations of the student experience are influenced by the point at which they are made (i.e. before the experience begins, whilst it is happening and after it has ended). It adopts a between-groups quantitative analysis of the affective evaluations made by 360 future, current and past postgraduate students of a UK business school. The study validates the proposition that affective forecasts and memories of the student experience are considerably inflated in prospect and retrospect; a finding that implies a significant impact bias. It is concluded that the impact bias may have important implications for influencing the effectiveness of student decision-making, the timing and comparability of student course evaluations, and understanding the nature and effects of word-of-mouth communication regarding the student experience

The evidence for automated grading in diabetic retinopathy screening

Peer reviewedPostprin

Re-formed by Kirk and Crown: urban politics and civic society in Glasgow during the reign of James VI, c.1585-1625

This thesis provides a history of the burgh of Glasgow during the adult reign of James VI (c.1585-1625). It is the first dedicated study of the burgh during this period and revises existing published work on Glasgow, which has tended to be teleological in choosing to focus on the way that developments in this period provided the basis for the town’s subsequent demographic and economic expansion in the late-seventeenth and eighteenth centuries. Here, the themes of Reformation and state formation are brought to the fore. The thesis argues that the period saw wholesale modernisation of Glasgow’s municipal administration and that this was driven by central government. The modernisation of local government in Glasgow is therefore used to support arguments about a ‘Stewart revolution in government’ and the ‘rise of the state’ under James VI. Between 1600 and 1606, the crown’s nominee as provost, Sir George Elphinstone of Blythswood, oversaw a wide-ranging programme of civic reform which established a constitution in the town that would last for more than a century. This period corresponded with the assertion of royal authority within the Kirk and the appointment of John Spottiswood as Archbishop of Glasgow in 1603. In discussing the impact of these developments upon Glasgow, the thesis also therefore provides the first examination of the ways in which the town experienced Scotland’s ‘Long Reformation’ and takes into account the activity of the Kirk there under both the Presbyterian and Episcopalian settlements. A new framework is offered for understanding the nature of change and continuity in Scotland’s late-sixteenth and early-seventeenth century burghs, which focuses more precisely on the change wrought by processes of state formation and Reformation than historians have done hitherto. In doing so, the thesis sheds new light on three important areas of Scotland’s early modern history: the emergence of the Scottish ‘early modern town’ during the reign of James VI, the Reformation and Jacobean state formation

Some aspects of the geomorphology of three Chiltern wind-gaps

Although there have been many publications dealing with the general geomorphology of the Central Chilterns and Vale of Aylesbury, none has yet dealt satisfactorily with the problems of the age, origin and development of the wind-gaps and their associated superficial deposits. Three gaps, the Wendover, Tring and Dagnall, have been selected for detailed study. The morphological features have been mapped on the 6 inch scale, the soils and gravels examined, and the pattern of soil series distribution related to the landforms. The various features of the gaps have then been compared, and suggestions made as to the possible evolution of the gaps. The principal hypotheses so far put forward postulate that the gaps were initiated: - 1) by pre-glacial rivers; 2) as glacial overflow channels; 3) by marine erosion in Pliocene times; 4) by pre-glacial rivers and modified by glacial melt-water. The last of these, with amplifications, seems most in accord with the field evidence accumulated in the course of the present study. Thus a hypothesis of major south-east flowing captured Mid-Tertiary consequents is submitted for the origin of the wind-gaps at Wendover and Tring. Subsequently each gap was affected by the Calabrian marine invasion and later modified both by glacial and periglacial processes. An early cold period in the Chilterns is suggested by the head deposits in the gaps and both drift and glacial gravels in the vicinity of the Tring Gap show that a tongue of Lowestoft Ice projected into the Vale of Aylesbury. At this stage, the gaps may have been modified by the passage of melt-water. The later Gipping Advance is represented by boulder clays to the north of the Vale, and extensive coombe deposits on its floor, and was possibly the agent responsible for the blocking and reversal of the former Ouzel-Dagnall consequent. Postglacially the drift has been all but removed and the coombe material dissected. In conclusion, it is shown that this sequence of events is generally compatible with that established in adjacent areas. <p

Can a single image processing algorithm work equally well across all phases of DCE-MRI?

Image segmentation and registration are said to be challenging when applied to dynamic contrast enhanced MRI sequences (DCE-MRI). The contrast agent causes rapid changes in intensity in the region of interest and elsewhere, which can lead to false positive predictions for segmentation tasks and confound the image registration similarity metric. While it is widely assumed that contrast changes increase the difficulty of these tasks, to our knowledge no work has quantified these effects. In this paper we examine the effect of training with different ratios of contrast enhanced (CE) data on two popular tasks: segmentation with nnU-Net and Mask R-CNN and registration using VoxelMorph and VTN. We experimented further by strategically using the available datasets through pretraining and fine tuning with different splits of data. We found that to create a generalisable model, pretraining with CE data and fine tuning with non-CE data gave the best result. This interesting find could be expanded to other deep learning based image processing tasks with DCE-MRI and provide significant improvements to the models performance