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A new method for fast quantitative mapping of absolute water content in vivo

NEUROimage 2006年第3期31卷 1156-1168页

作者： Neeb, H. Zilles, K. Shah, N. J. Forschungszentrum Julich Inst Med D-52425 Julich Germany Brain Imaging Ctr W D-52425 Julich Germany Univ Dortmund Inst Phys D-44221 Dortmund Germany

The presence of brain edema, in its various forms, is an accompanying feature of many diseased states. Although the localized occurrence of brain edema may be demonstrated with MRI, the quantitative determination of absolute water content, an aspect that could play an important role in the objective evaluation of the dynamics of brain edema and the monitoring of the efficiency of treatment, is much more demanding. We present a method for the localized and quantitative measurement of absolute water content based on the combination of two fast multi-slice and multi-time point sequences QUTE and TAPIR for mapping the T-2* and T-1 relaxation times, respectively. Incorporation of corrections for local B-1 field miscalibrations, temperature differences between the subject and a reference probe placed in the FOV, receiver profile inhomogeneities and T-1 saturation effects are included and allow the determination of water content with anatomical resolution and a precision > 98%. The method was validated in phantom studies and was applied to the localized in vivo measurement of water content in a group of normal individuals and a patient with brain tumor. The results demonstrate that in vivo measurement of regional absolute water content is possible in clinically relevant measurement times with a statistical and systematic measurement error of < 2%. (c) 2006 Elsevier Inc. All rights reserved.

关键词： Adult Brain Edema/diagnosis Brain Mapping/methods Brain Mapping/methods Brain Neoplasms/diagnosis Dominance, Cerebral/physiology Female Humans image processing, computer-assisted/methods Magnetic Resonance Imaging/methods Mathematical Computing Oligodendroglioma/diagnosis Phantoms, Imaging Sensitivity and Specificity Statistics as Topic

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Viral plaque analysis on a wide field-of-view, time-lapse, on-chip imaging platform

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ANALYST 2014年第15期139卷 3727-3734页

作者： Han, Chao Yang, Changhuei CALTECH Pasadena CA 91125 USA

The observation of viral plaques is the standard method for determining the viral titer and understanding the behaviors of viruses. Here, we report the application of a wide field-of-view (FOV), time-lapse, on-chip imaging platform, termed the ePetri, for plaque analysis of murine norovirus 1 (MNV-1). The ePetri offers the ability to dynamically track plaques at the individual cell death event level over a wide FOV of 6 mm x 4 mm. As demonstration, we captured high-resolution time-lapse images of MNV-1-infected cells at 30 min intervals. We implemented a customized image-processing program containing a density-based clustering algorithm to analyze the spatial-temporal distribution of cell death events to identify plaques at their earliest stages. By using the results in a viral titer count format, we showed that our approach gives results that are comparable to conventional plaque assays. We further showed that the extra information collected by the ePetri can be used to monitor the dynamics of plaque formation and growth. Finally, we performed a demonstration experiment to show the relevance of such an experimental format for viral inhibitor study. We believe the ePetri is a simple and compact solution for the automation of viral plaque assays, plaque behavior analysis, and antiviral drug discovery and study.

关键词： Algorithms Animals Caliciviridae Infections/pathology Caliciviridae Infections/veterinary Cell Line Equipment Design image processing, computer-assisted/methods Mice/virology Norovirus/growth & development Norovirus/isolation & purification Viral Plaque Assay/instrumentation

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An MRI-based atlas and database of the developing mouse brain

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NEUROimage 2011年第1期54卷 80-89页

作者： Chuang, Nelson Mori, Susumu Yamamoto, Akira Jiang, Hangyi Ye, Xin Xu, Xin Richards, Linda J. Nathans, Jeremy Miller, Michael I. Toga, Arthur W. Sidman, Richard L. Zhang, Jiangyang Johns Hopkins Univ Dept Radiol Sch Med Baltimore MD 21205 USA Johns Hopkins Univ Dept Mol Biol & Genet Sch Med Baltimore MD 21205 USA Johns Hopkins Univ Dept Biomed Engn Sch Med Baltimore MD 21205 USA Johns Hopkins Univ Howard Hughes Med Inst Sch Med Baltimore MD 21205 USA Kennedy Krieger Inst FM Kirby Funct Imaging Ctr Baltimore MD USA Univ Maryland Sch Med Dept Anat & Neurobiol Baltimore MD 21201 USA Univ Queensland Queensland Brain Inst Brisbane Qld Australia Univ Queensland Sch Biomed Sci Brisbane Qld Australia Johns Hopkins Univ Ctr Imaging Sci Baltimore MD 21205 USA Univ Calif Los Angeles Dept Neurol Lab Neuro Imaging Los Angeles CA 90024 USA Harvard Univ Beth Israel Deaconess Med Ctr Dept Neurol Sch Med Boston MA 02215 USA

The advent of mammalian gene engineering and genetically modified mouse models has led to renewed interest in developing resources for referencing and quantitative analysis of mouse brain anatomy. In this study, we used diffusion tensor imaging (DTI) for quantitative characterization of anatomical phenotypes in the developing mouse brain. As an anatomical reference for neuroscience research using mouse models, this paper presents DTI based atlases of ex vivo C57BL/6 mouse brains at several developmental stages. The atlas complements existing histology and MRI-based atlases by providing users access to three-dimensional, high-resolution images of the developing mouse brain, with distinct tissue contrasts and segmentations of major gray matter and white matter structures. The usefulness of the atlas and database was demonstrated by quantitative measurements of the development of major gray matter and white matter structures. Population average images of the mouse brain at several postnatal stages were created using large deformation diffeomorphic metric mapping and their anatomical variations were quantitatively characterized. The atlas and database enhance our ability to examine the neuroanatomy in normal or genetically engineered mouse strains and mouse models of neurological diseases. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Aging/physiology Animals Brain/anatomy & histology Brain/growth & development Brain Mapping/methods Brain Mapping/methods Cranial Nerves/anatomy & histology Cranial Nerves/growth & development Diffusion Tensor Imaging/methods Diffusion Tensor Imaging/methods image processing, computer-assisted/methods Imaging, Three-Dimensional/methods Magnetic Resonance Imaging/methods Mice Mice, Inbred C57BL Nerve Fibers/physiology Nerve Fibers/ultrastructure

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QUANTITATION OF AUTORADIOGRAPHIC GRAINS IN DIFFERENT ZONES OF ARTICULAR-CARTILAGE WITH image ANALYZER

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HISTOCHEMISTRY 1990年第3期93卷 241-245页

作者： PARKKINEN, JJ PAUKKONEN, K PESONEN, E LAMMI, MJ MARKKANEN, S HELMINEN, HJ TAMMI, M UNIV KUOPIO DEPT ANAT POB 6 SF-70211 KUOPIO FINLAND UNIV KUOPIO DEPT COMP SCI & APPL MATH SF-70211 KUOPIO FINLAND

A novel method is introduced for the estimation of grain numbers in autoradiographic sections of articular cartilage with an image analyzer. It is based on separation of grains from the underlying structures by gray level thresholding and determination of the percentage of total area occupied by grains in a relatively large measuring field. The mean grain size is used as a reference to calculate grain numbers per cell profile and per unit area of tissue in various zones of bovine articular cartilage labelled with 35S-sulphate in tissue culture. The results demonstrate considerable zonal differences as well as site related topographic variation in the rate of 35S-sulphate incorporation. The largest site-related variation in the grain counts was observed in the superficial zone, suggesting a delicate control of proteoglycan synthesis in this zone.

关键词： Animals Autoradiography Autoradiography Cartilage, Articular/analysis Cattle image processing, computer-assisted/methods Proteoglycans/metabolism Reproducibility of Results

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Investigating the predictive value of whole-brain structural MR scans in autism: A pattern classification approach

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NEUROimage 2010年第1期49卷 44-56页

作者： Ecker, Christine Rocha-Rego, Vanessa Johnston, Patrick Mourao-Miranda, Janaina Marquand, Andre Daly, Eileen M. Brammer, Michael J. Murphy, Clodagh Murphy, Declan G. Kings Coll London Inst Psychiat Dept Psychol Med Sect Brain Maturat London SE5 8AF England Univ Rio de Janeiro Inst Biophys Carlos Chagas Filho Rio De Janeiro Brazil Kings Coll London Inst Psychiat Dept Biostat Brain Image Anal Unit London SE5 8AF England

Autistic spectrum disorder (ASD) is accompanied by subtle and spatially distributed differences in brain anatomy that are difficult to detect using conventional mass-univariate methods (e.g., VBM). These require correction for multiple comparisons and hence need relatively large samples to attain sufficient statistical power. Reports of neuroanatomical differences from relatively small studies are thus highly variable. Also, VBM does not provide predictive value, limiting its diagnostic value. Here, we examined neuroanatomical networks implicated in ASD using a whole-brain classification approach employing a support vector machine (SVM) and investigated the predictive Value of structural MRI scans in adults with ASD. Subsequently, results were compared between SVM and VBM. We included 44 male adults;22 diagnosed with ASD using "gold-standard" research interviews and 22 healthy matched controls. SVM identified spatially distributed networks discriminating between ASD and controls. These included the limbic, frontal-striatal, fronto-temporal, fronto-parietal and cerebellar systems. SVM applied to gray matter scans correctly classified ASD individuals at a specificity of 86.0% and a sensitivity of 88.0%. Cases (68.0%) were correctly classified using white matter anatomy. The distance from the separating hyperplane (i.e., the test margin) was significantly related to current symptom severity. In contrast, VBM revealed few significant between-group differences at conventional levels of statistical stringency. We therefore suggest that SVM can detect subtle and spatially distributed differences in brain networks between adults with ASD and controls. Also, these differences provide significant predictive power for group membership, which is related to symptom severity. (c) 2009 Elsevier Inc. All rights reserved.

关键词： Adolescent Adult Artificial Intelligence Autistic Disorder/pathology Autistic Disorder/psychology Brain/pathology Brain Mapping Brain Mapping Female Humans image processing, computer-assisted/methods Magnetic Resonance Imaging/methods Male Neuropsychological Tests Pattern Recognition, Automated Predictive Value of Tests Reproducibility of Results Young Adult

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Automatically identifying and annotating mouse embryo gene expression patterns

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BIOINFORMATICS 2011年第8期27卷 1101-1107页

作者： Han, Liangxiu van Hemert, Jano I. Baldock, Richard A. Univ Edinburgh Sch Informat Edinburgh EH8 9AB Midlothian Scotland Inst Genet & Mol Med MRC Human Genet Unit Edinburgh EH4 2XU Midlothian Scotland

Motivation: Deciphering the regulatory and developmental mechanisms for multicellular organisms requires detailed knowledge of gene interactions and gene expressions. The availability of large datasets with both spatial and ontological annotation of the spatio-temporal patterns of gene expression in mouse embryo provides a powerful resource to discover the biological function of embryo organization. Ontological annotation of gene expressions consists of labelling images with terms from the anatomy ontology for mouse development. If the spatial genes of an anatomical component are expressed in an image, the image is then tagged with a term of that anatomical component. The current annotation is done manually by domain experts, which is both time consuming and costly. In addition, the level of detail is variable, and inevitably errors arise from the tedious nature of the task. In this article, we present a new method to automatically identify and annotate gene expression patterns in the mouse embryo with anatomical terms. Results: The method takes images from in situ hybridization studies and the ontology for the developing mouse embryo, it then combines machine learning and image processing techniques to produce classifiers that automatically identify and annotate gene expression patterns in these images. We evaluate our method on image data from the EURExpress study, where we use it to automatically classify nine anatomical terms: humerus, handplate, fibula, tibia, femur, ribs, petrous part, scapula and head mesenchyme. The accuracy of our method lies between 70% and 80% with few exceptions. We show that other known methods have lower classification performance than ours. We have investigated the images misclassified by our method and found several cases where the original annotation was not correct. This shows our method is robust against this kind of noise.

关键词： Animals Artificial Intelligence Embryo, Mammalian/anatomy & histology Embryo, Mammalian/metabolism Gene Expression Gene Expression Regulation, Developmental image processing, computer-assisted/methods In Situ Hybridization In Situ Hybridization Mice RNA/analysis RNA/metabolism

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Testing effects of signal transduction pathways on cadherin junctional complex assembly using quantitative image analysis

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methods 2003年第3期30卷 218-227页

作者： Gopalakrishnan, S Dunn, KW Marrs, JA Indiana Univ Sch Med Dept Med Indianapolis IN 46202 USA

Cadherin adhesion molecules function in numerous cell biological processes that influence embryo development, normal cell physiology, and pathophysiology of many disease processes. Cadherins nucleate the assembly of the adherens junction, a cell-to-cell adhesion plaque that is prominent in simple epithelial cells and found in many cell types. Numerous cell biological approaches have been used to study this interesting class of molecules. Here, we outline methodology used in our studies of junctional complexes to examine effects of signaling molecules on assembly mechanisms. This is a quantitative method that allows the investigator to test the combined effect of two different signaling processes to determine whether these two signals act in concert within the same pathway. We discuss how this method could be generalized to other studies to examine consequences of various experimental manipulations on the assembly of cellular structures. (C) 2003 Elsevier Science (USA). All rights reserved.

关键词： Cadherins/physiology Epithelial Cells/physiology image processing, computer-assisted/methods Intercellular Junctions/physiology Signal Transduction/physiology Software

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A Rapid and Efficient 2D/3D Nuclear Segmentation Method for Analysis of Early Mouse Embryo and Stem Cell image Data

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STEM CELL REPORTS 2014年第3期2卷 382-397页

作者： Lou, Xinghua Kang, Minjung Xenopoulos, Panagiotis Munoz-Descalzo, Silvia Hadjantonakis, Anna-Katerina Sloan Kettering Inst Dev Biol Program New York NY 10065 USA Cornell Univ Dept Biochem Cell & Mol Biol Program Weill Grad Sch Med Sci New York NY 10065 USA

Segmentation is a fundamental problem that dominates the success of microscopic image analysis. In almost 25 years of cell detection software development, there is still no single piece of commercial software that works well in practice when applied to early mouse embryo or stem cell image data. To address this need, we developed MINS (modular interactive nuclear segmentation) as a MATLAB/C++-based segmentation tool tailored for counting cells and fluorescent intensity measurements of 2D and 3D image data. Our aim was to develop a tool that is accurate and efficient yet straightforward and user friendly. The MINS pipeline comprises three major cascaded modules: detection, segmentation, and cell position classification. An extensive evaluation of MINS on both 2D and 3D images, and comparison to related tools, reveals improvements in segmentation accuracy and usability. Thus, its accuracy and ease of use will allow MINS to be implemented for routine single-cell-level image analyses.

关键词： Algorithms Animals Embryo, Mammalian/cytology Embryo, Mammalian/metabolism Embryonic Stem Cells/cytology Embryonic Stem Cells/metabolism image processing, computer-assisted/methods Imaging, Three-Dimensional/methods Mice Microscopy/methods Software

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Predicting EEG single trial responses with simultaneous fMRI and Relevance Vector Machine regression

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NEUROimage 2011年第2期56卷 826-836页

作者： De Martino, Federico de Borst, Aline W. Valente, Giancarlo Goebel, Rainer Formisano, Elia Maastricht Univ Dept Cognit Neurosci Fac Psychol & Neurosci Maastricht Netherlands Univ Pisa Dept Neurosci Pisa Italy

The combination of electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) has been proposed as a tool to study brain dynamics with both high temporal and high spatial resolution. Integration through EEG-fMRI trial-by-trial coupling has been proposed as a method to combine the different data sets and achieve temporal expansion of the fMRI data (Eichele et al., 2005). To fully benefit of this type of analysis simultaneous EEG-fMRI acquisitions are necessary (Debener et al., 2006). Here we address the issue of predicting the signal in one modality using information from the other modality. We use multivariate Relevance Vector Machine (RVM) regression to "learn" the relation between fMRI activation patterns and simultaneously acquired EEG responses in the context of a complex cognitive task entailing an auditory cue, visual mental imagery and a control visual target. We show that multivariate regression is a valuable approach for predicting evoked and induced oscillatory EEG responses from fMRI time series. Prediction of EEG from fMRI is largely influenced by the overall filtering effects of the hemodynamic response function. However, a detailed analysis of the auditory evoked responses shows that there is a small but significant contribution of single trial modulations that can be exploited for linking spatially-distributed patterns of fMRI activation to specific components of the simultaneously-recorded EEG signal. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Adult Artificial Intelligence Brain/physiology Brain Mapping/methods Brain Mapping/methods Electroencephalography/methods Female Humans image processing, computer-assisted/methods Magnetic Resonance Imaging/methods Signal processing, computer-assisted

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GP3: GenePix post-processing program for automated analysis of raw microarray data

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BIOINFORMATICS 2002年第5期18卷 771-773页

作者： Fielden, MR Halgren, RG Dere, E Zacharewski, TR Michigan State Univ Inst Environm Toxicol Natl Food Safety & Toxicol Ctr Dept Biochem & Mol Biol E Lansing MI 48824 USA

Here we describe an automated and customizable program to correct, filter and normalize raw microarray data captured using GenePix, a commonly used microarray image analysis application. Files can be processed individually or in batch mode for increased throughput. User defined inputs specify the stringency of data filtering and the method and conditions of normalization. The output includes gene summaries for replicate spots and descriptive statistics for each experiment. The source code (Perl) can also be adapted to handle raw data output from other image analysis applications.

关键词： Algorithms image processing, computer-assisted/methods Information Storage and Retrieval/methods Models, Statistical Oligonucleotide Array Sequence Analysis/methods Pattern Recognition, Automated Software

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