1,720,981 research outputs found
Automated radiological analysis of spinal MRI
Full text linkThis thesis addresses the problem of analysing clinical MRI using modern computer vision methods for a variety of clinical and research-related tasks. We use automated machine learning algorithms to develop a spinal MRI analysis framework for a number of tasks such as vertebrae detection, labelling; disc and vertebrae segmentation, and radiological grading, and we validate the framework on a large, heterogeneous dataset of 300 symptomatic back pain patients from multiple clinical sites and scanners. Our framework has a number of back pain research and other spine-related clinical applications and could hopefully find application in a clinical workflow in the future. Our framework has five steps -- detection, labelling, segmentation, support regions and features, and machine learning for radiological measurements. The framework works in full 3D and has currently been implemented on sagittal T2 slices. We use Deformable Part Models along with a chain model to detect and label vertebrae, and a powerful graph cuts based method for vertebrae and disc segmentation. The labelled detections and segmentations are used to place support regions for feature extraction, which are mapped into a number of radiological measurements -- namely Pfirrmann grade, disc space narrowing, and herniation/bulge. The radiological ground truth was provided by a clinical radiologist with 25 years experience. We demonstrate a high performance in the measurement in each. The measurements are performed using support vector machines and support vector regressors learned on training data. We next investigate the problem of what is the best method of obtaining support regions. We first used pixel intensity features to predict the Pfirrmann grade, narrowing and bulge/herniation, with vertebrae segmentation to localise their support regions. Since segmentation of spine images, especially intervertebral discs is an unsolved problem and algorithms are prone to failure, we then ask the question, to segment or not to segment. To answer the question, we compare results on Pfirrmann grade prediction with three different points on the no segmentation to full disc segmentation involving no segmentation, vertebrae segmentation, or disc segmentation and find that vertebrae segmentation suffices. We finally show preliminary results in distinguishing between different radiological conditions related to the posterior side of the disc more finely than before in literature, taking information from both sagittal and axial slices to attempt to distinguish between herniated and bulged discs
Automated analysis of spinal MRI using deep learning
Full text linkThe objective of this thesis is the automation of radiological gradings in spinal lumbar Magnetic Resonance Images (MRIs). Solving this is extremely beneficial as this in a way would help in the standardization of gradings especially for back pain research. The output of the research done in this thesis would allow extremely fast readings of clinical scans which can potentially be useful in a large scale epidemiological study of spine-related diseases and aide clinical decision making.
First, we build a pipeline to automatically produce radiological gradings of spinal lumbar MRIs and also localize the predicted pathologies. We show that: (i) a Con- volutional Neural Network (CNN) is able to predict multiple gradings at once, and we propose variants of the architecture including a multi-modal CNN that is able to take in both axial and sagittal or T1-weighted and T2-weighted scans; and (ii) a localization method that clearly shows pathological regions in the disc volumes using only a CNN trained for classification. The CNN is applied to a large corpus of standard clinical scan MRIs acquired from multiple machines via various scanning protocol, and is used to automatically compute intervertebral disc and vertebral body gradings for each MRI. We explore several radiological gradings: Pfirrmann grading, disc narrowing, upper/lower endplate defects, upper/lower marrow changes, spondy- lolisthesis, central canal stenosis, anterior/posterior disc bulging, and disc herniation. We report near human performances across all the gradings, and also visualize the evidence for these gradings localized on the original scans.
Then, since a significant proportion of patients scanned in a clinical setting have follow-up scans; we show that such longitudinal scans alone can be used as a form of âfreeâ self-supervision for training a deep network. We demonstrate this self- supervised learning for the case of T2-weighted sagittal lumbar MRIs. This learning via self-supervision can act as a pre-training regime when labelled data is sparse. We show that the performance of the pre-trained CNN on the supervised classification task is (i) superior to that of a network trained from scratch; and (ii) requires far fewer annotated training samples to reach an equivalent performance to that of the network trained from scratch.
Finally, we show some preliminary results in mapping disc features learnt from radiological gradings to the Oswestry Disability Index (ODI) which is a measure of disability commonly used by back pain patients.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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