Assessment of Functional Connectivity Impairment in Rat Brains
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Accurate Segmentation of Brain MR Images
Accurate segmentation of brain MR images Master of Science Thesis in Biomedical Engineering ANTONIO REYES PORRAS PÉREZ Department of Signals and Systems Division of Biomedical Engineering CHALMERS UNIVERSITY OF TECHNOLOGY Göteborg, Sweden, 2010 Report No. EX028/2010 Abstract Full brain segmentation has been of significant interest throughout the years. Recently, many research groups worldwide have been looking into development of patient-specific electromagnetic models for dipole source location in EEG. To obtain this model, accurate segmentation of various tissues and sub-cortical structures is thus required. In this project, the performance of three of the most widely used software packages for brain segmentation has been analyzed: FSL, SPM and FreeSurfer. For the analysis, real images from a patient and a set of phantom images have been used in order to evaluate the performance r of each one of these tools. Keywords: dipole source location, brain, patient-specific model, image segmentation, FSL, SPM, FreeSurfer. Acknowledgements To my advisor, Antony, for his guidance through the project. To my partner, Koushyar, for all the days we have spent in the hospital helping each other. To the staff in Sahlgrenska hospital for their collaboration. To MedTech West for this opportunity to learn. Table of contents 1. Introduction ......................................................................................................................................... 1 2. Magnetic resonance imaging .............................................................................................................. -
1 Structural and Functional Alterations in the Brain Gray Matter
Structural and functional alterations in the brain gray matter among first-degree relatives of schizophrenia patients: a multimodal meta-analysis of fMRI and VBM studies Running title: Familial risk for schizophrenia and alterations in the brain Aino I. L. Saarinen, PhD1,2,3,*, Sanna Huhtaniska, MD, PhD4, Juho Pudas, MB3, Lassi Björnholm, MD3, Tuomas Jukuri, MD, PhD3, Jussi Tohka, MD, PhD5, Niklas Granö, PhD6, Jennifer H. Barnett, PhD7,8, Vesa Kiviniemi, MD, PhD9,10, Juha Veijola, MD, PhD3,10,11, Mirka Hintsanen, PhD1, Johannes Lieslehto, MD, PhD 12 1 Research Unit of Psychology, University of Oulu, Finland 2 Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Finland 3 Research Unit of Clinical Neuroscience, Department of Psychiatry, University of Oulu, Finland 4 Center for Life Course Health Research, University of Oulu, Finland 5 A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland 6 Helsinki University Hospital, Department of Adolescent Psychiatry, Finland 7 Cambridge Cognition, Cambridge, UK 8 Department of Psychiatry, University of Cambridge, Cambridge, UK 9 Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland 10 Department of Psychiatry, Oulu University Hospital, Oulu, Finland 11 Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland 12 Section for Neurodiagnostic Applications, Department of Psychiatry, Ludwig Maximilian University, Nussbaumstrasse 7, 80336 Munich, Bavaria, * Corresponding author: Aino Saarinen. Department of Psychology and Logopedics, Faculty of Medicine, Haartmaninkatu 3, P.O. Box 21, 00014 University of Helsinki, Finland. E-mail: [email protected], Tel.: +35844 307 1204. 1 Abstract Objective: Schizophrenia has one of the highest heritability estimates in psychiatry, but the genetically- based underlying neuropathology has mainly remained unclear. -
Simultaneous PET/MRI for Connectivity Mapping: Quantitative Methods in Clinical Setting
UNIVERSITY OF PADOVA Department of Information Engineering Ph.D. School on Information Engineering Curriculum: Bioengineering - Cycle: XXX Simultaneous PET/MRI for Connectivity Mapping: Quantitative Methods in Clinical Setting Headmaster of the School: Prof. Andrea NEVIANI Supervisor: Prof. Alessandra BERTOLDO PhD. Candidate: Eng. Erica SILVESTRI 2018 iii University of Padova Abstract Ph.D. School on Information Engineering Simultaneous PET/MRI for Connectivity Mapping: Quantitative Methods in Clinical Setting by Eng. Erica SILVESTRI In recent years, the study of brain connectivity has received growing inter- est from neuroscience field, from a point of view both of analysis of patho- logical condition and of a healthy brain. Hybrid PET/MRI scanners are promising tools to study this complex phenomenon. This thesis presents a general framework for the acquisition and analysis of simultaneous multi- modal PET/MRI imaging data to study brain connectivity in a clinical set- ting. Several aspects are faced ranging from the planning of an acquisition protocol consistent with clinical constraint to the off-line PET image recon- struction, from the selection and implementation of methods for quantifying the acquired data to the development of methodologies to combine the com- plementary informations obtained with the two modalities. The developed analysis framework was applied to two different studies, a first conducted on patients affected by Parkinson’s Disease and dementia, and a second one on high grade gliomas, as proof of concept evaluation that the pipeline can be extended in clinical settings. v Università degli Studi di Padova Sommario Scuola di Dottorato in Ingegneria dell’Informazione Acquisizioni simultanee PET/MR per lo studio della connettività: metodi quantitativi in ambito clinico di Ing. -
An Introduction to Image Analysis Using Imagej
An introduction to image analysis using ImageJ Mark Willett, Imaging and Microscopy Centre, Biological Sciences, University of Southampton. Pete Johnson, Biophotonics lab, Institute for Life Sciences University of Southampton. 1 “Raw Images, regardless of their aesthetics, are generally qualitative and therefore may have limited scientific use”. “We may need to apply quantitative methods to extrapolate meaningful information from images”. 2 Examples of statistics that can be extracted from image sets . Intensities (FRET, channel intensity ratios, target expression levels, phosphorylation etc). Object counts e.g. Number of cells or intracellular foci in an image. Branch counts and orientations in branching structures. Polarisations and directionality . Colocalisation of markers between channels that may be suggestive of structure or multiple target interactions. Object Clustering . Object Tracking in live imaging data. 3 Regardless of the image analysis software package or code that you use….. • ImageJ, Fiji, Matlab, Volocity and IMARIS apps. • Java and Python coding languages. ….image analysis comprises of a workflow of predefined functions which can be native, user programmed, downloaded as plugins or even used between apps. This is much like a flow diagram or computer code. 4 Here’s one example of an image analysis workflow: Apply ROI Choose Make Acquisition Processing to original measurement measurements image type(s) Thresholding Save to ROI manager Make binary mask Make ROI from binary using “Create selection” Calculate x̄, Repeat n Chart data SD, TTEST times and interpret and Δ 5 A few example Functions that can inserted into an image analysis workflow. You can mix and match them to achieve the analysis that you want. -
Bio-Formats Documentation Release 4.4.9
Bio-Formats Documentation Release 4.4.9 The Open Microscopy Environment October 15, 2013 CONTENTS I About Bio-Formats 2 1 Why Java? 4 2 Bio-Formats metadata processing 5 3 Help 6 3.1 Reporting a bug ................................................... 6 3.2 Troubleshooting ................................................... 7 4 Bio-Formats versions 9 4.1 Version history .................................................... 9 II User Information 23 5 Using Bio-Formats with ImageJ and Fiji 24 5.1 ImageJ ........................................................ 24 5.2 Fiji .......................................................... 25 5.3 Bio-Formats features in ImageJ and Fiji ....................................... 26 5.4 Installing Bio-Formats in ImageJ .......................................... 26 5.5 Using Bio-Formats to load images into ImageJ ................................... 28 5.6 Managing memory in ImageJ/Fiji using Bio-Formats ................................ 32 5.7 Upgrading the Bio-Formats importer for ImageJ to the latest trunk build ...................... 34 6 OMERO 39 7 Image server applications 40 7.1 BISQUE ....................................................... 40 7.2 OME Server ..................................................... 40 8 Libraries and scripting applications 43 8.1 Command line tools ................................................. 43 8.2 FARSIGHT ...................................................... 44 8.3 i3dcore ........................................................ 44 8.4 ImgLib ....................................................... -
Official Program
OFFICIAL PROGRAM ___________________________________________________________________ Generously Sponsored by: TABLE OF CONTENTS SCHEDULE 3 KEYNOTE SPEAKERS 5 BREAKOUT SESSIONS 7 ORAL PRESENTATIONS 11 POSTER SESSION 1 (ORGANIZED BY POSTER NUMBER) 13 POSTER SESSION 2 (ORGANIZED BY POSTER NUMBER) 16 ABSTRACTS – ORAL PRESENTATIONS 19 ABSTRACTS – POSTER SESSION 1 44 ABSTRACTS – POSTER SESSION 2 91 SEMSS ORGANIZING COMMITTEE 135 LIGHT HALL & CAMPUS MAPS 136 SEMSS encourages open and honest intellectual debate as part of a welcoming and inclusive atmosphere at every conference. SEMSS asks each participant to foster rigorous analysis of all science presented or discussed in a manner respectful to all conferees. To help maintain an open and respectful community of scientists, SEMSS does not tolerate illegal or inappropriate behavior at any conference site, including violations of applicable laws pertaining to sale or consumption of alcohol, destruction of property, or harassment of any kind, including sexual harassment. SEMSS condemns inappropriate or suggestive acts or comments that demean another person by reason of his or her gender, gender identity or expression, race, religion, ethnicity, age or disability or that are unwelcome or offensive to other members of the community or their guests. * *Adapted from the language of Gordon Research Conferences 2 SEMSS 2018 SCHEDULE SATURDAY, NOVEMEBER 10, 2018 _____________________________________________________________________________________ REGISTRATION 12:30 – 1:00 PM Location: Light Hall, North -
A Simple Rapid Process for Semi-Automated Brain Extraction from Magnetic Resonance Images of the Whole Mouse Head
Accepted Manuscript Title: A simple rapid process for semi-automated brain extraction from magnetic resonance images of the whole mouse head Author: Adam Delora Aaron Gonzales Christopher S. Medina Adam Mitchell Abdul Faheem Mohed Russell E. Jacobs Elaine L. Bearer PII: S0165-0270(15)00370-2 DOI: http://dx.doi.org/doi:10.1016/j.jneumeth.2015.09.031 Reference: NSM 7360 To appear in: Journal of Neuroscience Methods Received date: 10-8-2015 Revised date: 28-9-2015 Accepted date: 30-9-2015 Please cite this article as: Delora A, Gonzales A, Medina CS, Mitchell A, Mohed AF, Jacobs RE, Bearer EL, A simple rapid process for semi-automated brain extraction from magnetic resonance images of the whole mouse head, Journal of Neuroscience Methods (2015), http://dx.doi.org/10.1016/j.jneumeth.2015.09.031 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Graphical Abstract (for review) Accepted Manuscript Page 1 of 27 A simple rapid process for semi-automated brain extraction from magnetic resonance images of the whole mouse head Delora, Gonzales, Medina, Mitchell, Mohed, Jacobs and Bearer Highlights • We present a new software tool for automated mouse brain extraction • The new software tool is rapid and applies to any MR dataset • We validate the output by comparing with manual extraction (the gold standard) • Brain extraction with this tool preserves individual volume, and improves alignments Accepted Manuscript Page 2 of 27 Fast automated skull-stripping Delora et al. -
Ilastik: Interactive Machine Learning for (Bio)Image Analysis
ilastik: interactive machine learning for (bio)image analysis Stuart Berg1, Dominik Kutra2,3, Thorben Kroeger2, Christoph N. Straehle2, Bernhard X. Kausler2, Carsten Haubold2, Martin Schiegg2, Janez Ales2, Thorsten Beier2, Markus Rudy2, Kemal Eren2, Jaime I Cervantes2, Buote Xu2, Fynn Beuttenmueller2,3, Adrian Wolny2, Chong Zhang2, Ullrich Koethe2, Fred A. Hamprecht2, , and Anna Kreshuk2,3, 1HHMI Janelia Research Campus, Ashburn, Virginia, USA 2HCI/IWR, Heidelberg University, Heidelberg, Germany 3European Molecular Biology Laboratory, Heidelberg, Germany We present ilastik, an easy-to-use interactive tool that brings are computed and passed on to a powerful nonlinear algo- machine-learning-based (bio)image analysis to end users with- rithm (‘the classifier’), which operates in the feature space. out substantial computational expertise. It contains pre-defined Based on examples of correct class assignment provided by workflows for image segmentation, object classification, count- the user, it builds a decision surface in feature space and ing and tracking. Users adapt the workflows to the problem at projects the class assignment back to pixels and objects. In hand by interactively providing sparse training annotations for other words, users can parametrize such workflows just by a nonlinear classifier. ilastik can process data in up to five di- providing the training data for algorithm supervision. Freed mensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allow- from the necessity to understand intricate algorithm details, ing for interactive prediction on data larger than RAM. Once users can thus steer the analysis by their domain expertise. the classifiers are trained, ilastik workflows can be applied to Algorithm parametrization through user supervision (‘learn- new data from the command line without further user interac- ing from training data’) is the defining feature of supervised tion. -
Modelling of the Effects of Entrainment Defects on Mechanical Properties in Al-Si-Mg Alloy Castings
MODELLING OF THE EFFECTS OF ENTRAINMENT DEFECTS ON MECHANICAL PROPERTIES IN AL-SI-MG ALLOY CASTINGS By YANG YUE A dissertation submitted to University of Birmingham for the degree of DOCTOR OF PHILOSOPHY School of Metallurgy and Materials University of Birmingham June 2014 University of Birmingham Research Archive e-theses repository This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder. Abstract Liquid aluminium alloy is highly reactive with the surrounding atmosphere and therefore, surface films, predominantly surface oxide films, easily form on the free surface of the melt. Previous researches have highlighted that surface turbulence in liquid aluminium during the mould-filling process could result in the fold-in of the surface oxide films into the bulk liquid, and this would consequently generate entrainment defects, such as double oxide films and entrapped bubbles in the solidified casting. The formation mechanisms of these defects and their detrimental e↵ects on both mechani- cal properties and reproducibility of properties of casting have been studied over the past two decades. However, the behaviour of entrainment defects in the liquid metal and their evolution during the casting process are still unclear, and the distribution of these defects in casting remains difficult to predict. -
Designing GPU-Accelerated Image Data Flow Graphs for CLIJ2 and Clesperanto and Deployment to Imagej, Fiji, Icy, Matlab, Qupath, Python, Napari and C++
Designing GPU-accelerated Image Data Flow Graphs for CLIJ2 and clEsperanto and deployment to ImageJ, Fiji, Icy, Matlab, QuPath, Python, Napari and C++ Tutors: Robert Haase ([email protected]) Stephane´ Rigaud ([email protected]) Session 1: 2020-12-01 16:00 UTC – 2020-12-01 20:00 UTC Session 2: 2020-12-02 09:00 UTC – 2020-12-02 13:00 UTC Designing GPU-accelerated Image Data Flow Graphs for CLIJ2 and clEsperanto and deployment to ImageJ, Fiji, Icy, Matlab, QuPath, Python, Napari and C++ Robert Haase123, Stéphane Rigaud4 1 Center for Systems Biology Dresden 2 Max Planck Institute for Molecular Cell Biology and Genetics Dresden 3 Physics of Life Cluster of Excellence, TU Dresden 4 Image Analysis Hub, C2RT, Institut Pasteur, Paris Abstract The current rise of graphics processing units (GPUs) in the context of image processing boosts the need for accessible tools for building GPU-accelerated image analysis workflows. Typically, designing data analysis procedures utilizing GPUs involves coding skills and knowledge of GPU-specific programming languages such as the Open Computing Language (OpenCL) [1]. For facilitating end-user access to modern computing hardware such as GPUs, the CLIJ platform [2] was developed and documented in detail [3, 4]. It targets programming beginners through an abstraction layer that allows to call GPU- accelerated image processing operations without the need for learning a new programming language such as OpenCL. To further lower the entrance bounden, a recently introduced a graphical user interface for the Fiji platform [5] utilizes an image data flow graph for designing image processing workflows interactively on screen [6]. -
FIB-SEM) Tomography Hui Yuan, B Van De Moortele, Thierry Epicier
Accurate post-mortem alignment for Focused Ion Beam and Scanning Electron Microscopy (FIB-SEM) tomography Hui Yuan, B van de Moortele, Thierry Epicier To cite this version: Hui Yuan, B van de Moortele, Thierry Epicier. Accurate post-mortem alignment for Focused Ion Beam and Scanning Electron Microscopy (FIB-SEM) tomography. 2020. hal-03024445 HAL Id: hal-03024445 https://hal.archives-ouvertes.fr/hal-03024445 Preprint submitted on 25 Nov 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Accurate post-mortem alignment for Focused Ion Beam and Scanning Electron Microscopy (FIB-SEM) tomography H. Yuan1, 2*, B. Van de Moortele2, T. Epicier1,3 § 1. Université de Lyon, INSA-Lyon, Université Claude Bernard Lyon1, MATEIS, umr CNRS 5510, 69621 Villeurbanne Cedex, France 2. Université de Lyon, ENS-Lyon, LGLTPE, umr CNRS 5276, 69364 Lyon 07, France 3. Université de Lyon, Université Claude Bernard Lyon1, IRCELYON, umr CNRS 5256, 69626 Villeurbanne Cedex, France Abstract: Drifts in the three directions (X, Y, Z) during the FIB-SEM slice-and-view tomography is an important issue in 3D-FIB experiments which may induce significant inaccuracies in the subsequent volume reconstruction and further quantification of morphological volume parameters of the sample microstructure. -
Image Analysis Software Options for Use at Home
Image Analysis Software Options for Use at Home Supported Software (CALMN and MAGIC) FIJI (Fiji is just image J) https://fiji.sc/ Open source (Free) software. Compatible with Window, Mac, Linux. Minimal requirement on computer specs and easy to learn. Online tutorial for self-learning: https://imagej.net/Using_Fiji Bitplane Imaris This is an analysis software offered in the facility, which has a high requirement of computer specs. To use Imaris at home, here are a few options: • Imaris has a basic “Viewer” that can be downloaded and used for free. There are recommended minimum system requirements, but looking at small data sets should be possible on most computers. This is a great way to look at images and share imaging with your colleagues and lab members. Click here to download the FREE Imaris Viewer and see the system recommendations. • The CALMN and MAGIC facilities also have access to a very limited number of satellite licenses that can be accessed off site. This software can run on PC or Mac, minimum system requirements can be found here. If you have access to computer that meets the minimum requirements and would like to use one of these licenses, please contact Kaye Thomas or Yurong Gao directly. Depending on the number of requests, we will make a schedule for using the software, most likely for 2-3 days at a time. Nikon NIS-Elements This is the software used for image acquisition on the new Nikon confocal microscope. It also has many image analysis tools. • Nikon offers free Viewer is available for both Windows and Mac systems here.