3,839 research outputs found
Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging
Many analyses of neuroimaging data involve studying one or more regions of
interest (ROIs) in a brain image. In order to do so, each ROI must first be
identified. Since every brain is unique, the location, size, and shape of each
ROI varies across subjects. Thus, each ROI in a brain image must either be
manually identified or (semi-) automatically delineated, a task referred to as
segmentation. Automatic segmentation often involves mapping a previously
manually segmented image to a new brain image and propagating the labels to
obtain an estimate of where each ROI is located in the new image. A more recent
approach to this problem is to propagate labels from multiple manually
segmented atlases and combine the results using a process known as label
fusion. To date, most label fusion algorithms either employ voting procedures
or impose prior structure and subsequently find the maximum a posteriori
estimator (i.e., the posterior mode) through optimization. We propose using a
fully Bayesian spatial regression model for label fusion that facilitates
direct incorporation of covariate information while making accessible the
entire posterior distribution. We discuss the implementation of our model via
Markov chain Monte Carlo and illustrate the procedure through both simulation
and application to segmentation of the hippocampus, an anatomical structure
known to be associated with Alzheimer's disease.Comment: 24 pages, 10 figure
Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.
Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome
Psychophysiology-based QoE assessment : a survey
We present a survey of psychophysiology-based assessment for quality of experience (QoE) in advanced multimedia technologies. We provide a classification of methods relevant to QoE and describe related psychological processes, experimental design considerations, and signal analysis techniques. We summarize multimodal techniques and discuss several important aspects of psychophysiology-based QoE assessment, including the synergies with psychophysical assessment and the need for standardized experimental design. This survey is not considered to be exhaustive but serves as a guideline for those interested to further explore this emerging field of research
Recommended from our members
A Survey on Nature-Inspired Medical Image Analysis: A Step Further in Biomedical Data Integration
Graph Representation Learning in Biomedicine
Biomedical networks are universal descriptors of systems of interacting
elements, from protein interactions to disease networks, all the way to
healthcare systems and scientific knowledge. With the remarkable success of
representation learning in providing powerful predictions and insights, we have
witnessed a rapid expansion of representation learning techniques into
modeling, analyzing, and learning with such networks. In this review, we put
forward an observation that long-standing principles of networks in biology and
medicine -- while often unspoken in machine learning research -- can provide
the conceptual grounding for representation learning, explain its current
successes and limitations, and inform future advances. We synthesize a spectrum
of algorithmic approaches that, at their core, leverage graph topology to embed
networks into compact vector spaces, and capture the breadth of ways in which
representation learning is proving useful. Areas of profound impact include
identifying variants underlying complex traits, disentangling behaviors of
single cells and their effects on health, assisting in diagnosis and treatment
of patients, and developing safe and effective medicines
CT Radiomic Features and Clinical Biomarkers for Predicting Coronary Artery Disease
This study was aimed to investigate the predictive value of the radiomics features extracted from pericoronaric adipose tissue & mdash; around the anterior interventricular artery (IVA) & mdash; to assess the condition of coronary arteries compared with the use of clinical characteristics alone (i.e., risk factors). Clinical and radiomic data of 118 patients were retrospectively analyzed. In total, 93 radiomics features were extracted for each ROI around the IVA, and 13 clinical features were used to build different machine learning models finalized to predict the impairment (or otherwise) of coronary arteries. Pericoronaric radiomic features improved prediction above the use of risk factors alone. In fact, with the best model (Random Forest + Mutual Information) the AUROC reached 0.820 +/- 0.076 . As a matter of fact, the combined use of both types of features (i.e., radiomic and clinical) allows for improved performance regardless of the feature selection method used. Experimental findings demonstrated that the use of radiomic features alone achieves better performance than the use of clinical features alone, while the combined use of both clinical and radiomic biomarkers further improves the predictive ability of the models. The main contribution of this work concerns: (i) the implementation of multimodal predictive models, based on both clinical and radiomic features, and (ii) a trusted system to support clinical decision-making processes by means of explainable classifiers and interpretable features
Toward a morphodynamic model of the cell: Signal processing for cell modeling
From a systems biology perspective, the cell is the principal element of information integration. Therefore, understanding the cell in its spatiotemporal context is the key to unraveling many of the still unknown mechanisms of life and disease. This article reviews image processing aspects relevant to the quantification of cell morphology and dynamics. We cover both acquisition (hardware) and analysis (software) related issues, in a multiscale fashion, from the detection of cellular components to the description of the entire cell in relation to its extracellular environment. We then describe ongoing efforts to integrate all this vast and diverse information along with data about the biomechanics of the cell to create a credible model of cell morphology and behavior.Carlos Ortiz-de-Solorzano and Arrate Muñoz-Barrutia were supported by the Spanish Ministry of Economy and Competitiveness grants with reference DPI2012-38090-C03-02 and TEC2013-48552-C02, respectively. Michal Kozubek was supported by the Czech Science Foundation (302/12/G157)
Recent developments in X-ray diffraction/scattering computed tomography for materials science
X-ray diffraction/scattering computed tomography (XDS-CT) methods are a non-destructive class of chemical imaging techniques that have the capacity to provide reconstructions of sample cross-sections with spatially resolved chemical information. While X-ray diffraction CT (XRD-CT) is the most well-established method, recent advances in instrumentation and data reconstruction have seen greater use of related techniques like small angle X-ray scattering CT and pair distribution function CT. Additionally, the adoption of machine learning techniques for tomographic reconstruction and data analysis are fundamentally disrupting how XDS-CT data is processed. The following narrative review highlights recent developments and applications of XDS-CT with a focus on studies in the last five years. This article is part of the theme issue 'Exploring the length scales, timescales and chemistry of challenging materials (Part 2)'
- …