330,506 research outputs found

    Cumulative Subject Index to Volumes 1-21

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    Subject Index to Vertebrate Pest Conference Proceedings, volumes 1–21 (1962–2004)

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    This is a 409-page (searchable) document. Listings are by subjects, arranged alphabetically, and show all authors, full titles, and complete citations

    Bibliometric studies on single journals: a review

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    This paper covers a total of 82 bibliometric studies on single journals (62 studies cover unique titles) published between 1998 and 2008 grouped into the following fields; Arts, Humanities and Social Sciences (12 items); Medical and Health Sciences (19 items); Sciences and Technology (30 items) and Library and Information Sciences (21 items). Under each field the studies are described in accordance to their geographical location in the following order, United Kingdom, United States and Americana, Europe, Asia (India, Africa and Malaysia). For each study, elements described are (a) the journal’s publication characteristics and indexation information; (b) the objectives; (c) the sampling and bibliometric measures used; and (d) the results observed. A list of journal titles studied is appended. The results show that (a)bibliometric studies cover journals in various fields; (b) there are several revisits of some journals which are considered important; (c) Asian and African contributions is high (41.4 of total studies; 43.5 covering unique titles), United States (30.4 of total; 31.0 on unique titles), Europe (18.2 of total and 14.5 on unique titles) and the United Kingdom (10 of total and 11 on unique titles); (d) a high number of bibliometrists are Indians and as such coverage of Indian journals is high (28 of total studies; 30.6 of unique titles); and (e) the quality of the journals and their importance either nationally or internationally are inferred from their indexation status

    Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach

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    Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-constrained bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localisation tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, the refinement step is designed to explicitly enforce a shape constraint and improve segmentation quality. This step is effective for overcoming image artefacts (e.g. due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The proposed pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution and anatomically smooth bi-ventricular 3D models, despite the artefacts in input CMR volumes

    Special Libraries, March 1955

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    Volume 46, Issue 3https://scholarworks.sjsu.edu/sla_sl_1955/1002/thumbnail.jp

    Evaluation of atlas-based segmentation of hippocampi in healthy humans

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    Introduction and aim: Region of interest (ROI)-based functional magnetic resonance imaging (fMRI) data analysis relies on extracting signals from a specific area which is presumed to be involved in the brain activity being studied. The hippocampus is of interest in many functional connectivity studies for example in epilepsy as it plays an important role in epileptogenesis. In this context, ROI may be defined using different techniques. Our study aims at evaluating the spatial correspondence of hippocampal ROIs obtained using three brain atlases with hippocampal ROI obtained using an automatic segmentation algorithm dedicated to the hippocampus. Material and methods: High-resolution volumetric T1-weighted MR images of 18 healthy volunteers (five females) were acquired on a 3T scanner. Individual ROIs for both hippocampi of each subject were segmented from the MR images using an automatic hippocampus and amygdala segmentation software called SACHA providing the gold standard ROI for comparison with the atlas-derived results. For each subject, hippocampal ROIs were also obtained using three brain atlases: PickAtlas available as a commonly used software toolbox; automated anatomical labeling (AAL) atlas included as a subset of ROI into PickAtlas toolbox and a frequency-based brain atlas by Hammers et al. The levels of agreement between the SACHA results and those obtained using the atlases were assessed based on quantitative indices measuring volume differences and spatial overlap. The comparison was performed in standard Montreal Neurological Institute space, the registration being obtained with SPM5 (http://www.fil.ion.ucl.ac.uk/spm/). Results: The mean volumetric error across all subjects was 73% for hippocampal ROIs derived from AAL atlas; 20% in case of ROIs derived from the Hammers atlas and 107% for ROIs derived from PickAtlas. The mean false-positive and false-negative classification rates were 60% and 10% respectively for the AAL atlas; 16% and 32% for the Hammers atlas and 6% and 72% for the PickAtlas. Conclusion: Though atlas-based ROI definition may be convenient, the resulting ROIs may be poor representations of the hippocampus in some studies critical to under- or oversampling. Performance of the AAL atlas was inferior to that of the Hammers atlas. Hippocampal ROIs derived from PickAtlas are highly significantly smaller, and this results in the worst performance out of three atlases. It is advisable that the defined ROIs should be verified with knowledge of neuroanatomy before using it for further data analysis

    Edited Volumes, Monographs, and Book Chapters in the Book Citation Index (BKCI) and Science Citation Index (SCI, SoSCI, A&HCI)

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    In 2011, Thomson-Reuters introduced the Book Citation Index (BKCI) as part of the Science Citation Index (SCI). The interface of the Web of Science version 5 enables users to search for both "Books" and "Book Chapters" as new categories. Books and book chapters, however, were always among the cited references, and book chapters have been included in the database since 2005. We explore the two categories with both BKCI and SCI, and in the sister social sciences (SoSCI) and the arts & humanities (A&HCI) databases. Book chapters in edited volumes can be highly cited. Books contain many citing references but are relatively less cited. This may find its origin in the slower circulation of books than of journal articles. It is possible to distinguish between monographs and edited volumes among the "Books" scientometrically. Monographs may be underrated in terms of citation impact or overrated using publication performance indicators because individual chapters are counted as contributions separately in terms of articles, reviews, and/or book chapters.Comment: Journal of Scientometric Research, 2012, in pres

    Special Libraries, February 1951

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    Volume 42, Issue 2https://scholarworks.sjsu.edu/sla_sl_1951/1001/thumbnail.jp

    Special Libraries, December 1950

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    Volume 41, Issue 10https://scholarworks.sjsu.edu/sla_sl_1950/1009/thumbnail.jp
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