1,720,962 research outputs found
CEREBRUM-7T: Fast and Fully Volumetric Brain Segmentation of 7 Tesla MR Volumes
Full text linkUltra-high-field magnetic resonance imaging (MRI) enables sub-millimetre resolution imaging of the human brain, allowing the study of functional circuits of cortical layers at the meso-scale. An essential step in many functional and structural neuroimaging studies is segmentation, the operation of partitioning the MR images in anatomical structures. Despite recent efforts in brain imaging analysis, the literature lacks in accurate and fast methods for segmenting 7-tesla (7T) brain MRI. We here present CEREBRUM-7T, an optimised end-to-end convolutional neural network, which allows fully automatic segmentation of a whole 7T T1w MRI brain volume at once, without partitioning the volume, pre-processing, nor aligning it to an atlas. The trained model is able to produce accurate multi-structure segmentation masks on six different classes plus background in only a few seconds. The experimental part, a combination of objective numerical evaluations and subjective analysis, confirms that the proposed solution outperforms the training labels it was trained on and is suitable for neuroimaging studies, such as layer functional MRI studies. Taking advantage of a fine-tuning operation on a reduced set of volumes, we also show how it is possible to effectively apply CEREBRUM-7T to different sites data. Furthermore, we release the code, 7T data, and other materials, including the training labels and the Turing test
Fighting the scanner effect in brain MRI segmentation with a progressive level-of-detail network trained on multi-site data
Full text linkMany clinical and research studies of the human brain require accurate structural MRI segmentation. While traditional atlas-based methods can be applied to volumes from any acquisition site, recent deep learning algorithms ensure high accuracy only when tested on data from the same sites exploited in training (i.e., internal data). Performance degradation experienced on external data (i.e., unseen volumes from unseen sites) is due to the inter-site variability in intensity distributions, and to unique artefacts caused by different MR scanner models and acquisition parameters. To mitigate this site-dependency, often referred to as the scanner effect, we propose LOD-Brain, a 3D convolutional neural network with progressive levels-of-detail (LOD), able to segment brain data from any site. Coarser network levels are responsible for learning a robust anatomical prior helpful in identifying brain structures and their locations, while finer levels refine the model to handle site-specific intensity distributions and anatomical variations. We ensure robustness across sites by training the model on an unprecedentedly rich dataset aggregating data from open repositories: almost 27,000 T1w volumes from around 160 acquisition sites, at 1.5 - 3T, from a population spanning from 8 to 90 years old. Extensive tests demonstrate that LOD-Brain produces state-of-the-art results, with no significant difference in performance between internal and external sites, and robust to challenging anatomical variations. Its portability paves the way for large-scale applications across different healthcare institutions, patient populations, and imaging technology manufacturers. Code, model, and demo are available on the project website
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
OPTIMIZATION OF HTC VALUE BY INVERSE MODEL BASED ON EXPERIMENTAL MEASUREMENT OF A 26.6-ton STEEL INGOT
No full textA 26.6-ton steel ingot has been experimentally investigated during the whole industrial casting process to determine the heat transfer coefficient (HTC) with the mould as a function of the temperature. The determination of this parameter is strongly important for the correct setting of numerical simulations because it greatly influences the solidification and, therefore, the occurrence of defects in steel ingots, such as shrinkage porosities and segregations. Temperature variations of eight distinct positions inside the mould were recorded to acquire thermal conditions and determine the HTC value at the interface between ingot and mould. The calculation was carried out through the inverse model implemented in the ProCAST® 2022 simulation software. All parameters and boundary conditions of the industrial process were evaluated and recorded during the filling and solidification steps. The thermal properties of the materials used in the model were previously measured in laboratory
RISK ASSESSMENT OF CASTING POWDERS ENTRAPMENT IN A 26 TONNE STEEL INGOT USING NUMERICAL SIMULATION
No full textNowadays, steelmaking companies use numerical simulations as an advanced tool to optimize process parameters, reduce defects, and enhance product quality, aligning with the rising market demand for high-quality steel ingots. For more accurate simulations, it is essential to set the correct material properties, process parameters (such as flow rate and temperatures), heat transfer coefficient, as well as the suitable geometry and corresponding discretization. During the mold filling, one main issue is the trapping in the liquid of casting powders used to protect the steel from reoxidation, which turns in the presence of large non-metallic inclusions in the solidified part and the consequent discard. This work is part of an industrial research aimed at studying the filling and solidification of a 26 tonne square ingot by using numerical simulation with the commercial program ProCAST®. The study investigates the impact of several casting parameters and liquid upgate system geometries on the creation of exogenous non-metallic inclusions through simplified numerical simulations. The reliability of the simulation model was evaluated by an experimental test conducted on an industrial scale: the inclusions measured were all less than the acceptable threshold of <2.0 mm equivalent defect. The research was able to establish the conditions needed to prevent the sporadic entrapment of casting powders, hence enhancing the overall quality of this type of ingot
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
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