Literature DB >> 26739264

Segmentation of human brain using structural MRI.

Gunther Helms1.   

Abstract

Segmentation of human brain using structural MRI is a key step of processing in imaging neuroscience. The methods have undergone a rapid development in the past two decades and are now widely available. This non-technical review aims at providing an overview and basic understanding of the most common software. Starting with the basis of structural MRI contrast in brain and imaging protocols, the concepts of voxel-based and surface-based segmentation are discussed. Special emphasis is given to the typical contrast features and morphological constraints of cortical and sub-cortical grey matter. In addition to the use for voxel-based morphometry, basic applications in quantitative MRI, cortical thickness estimations, and atrophy measurements as well as assignment of cortical regions and deep brain nuclei are briefly discussed. Finally, some fields for clinical applications are given.

Entities:  

Keywords:  Brain; Cortical thickness; MRI; Morphometry; Segmentation

Mesh:

Year:  2016        PMID: 26739264     DOI: 10.1007/s10334-015-0518-z

Source DB:  PubMed          Journal:  MAGMA        ISSN: 0968-5243            Impact factor:   2.310


  77 in total

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Review 5.  Post-processing of structural MRI for individualized diagnostics.

Authors:  Pascal Martin; Benjamin Bender; Niels K Focke
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Review 7.  Multi-atlas segmentation of biomedical images: A survey.

Authors:  Juan Eugenio Iglesias; Mert R Sabuncu
Journal:  Med Image Anal       Date:  2015-07-06       Impact factor: 8.545

8.  Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine.

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9.  A mean three-dimensional atlas of the human thalamus: generation from multiple histological data.

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Review 10.  Relevance of magnetic resonance imaging for early detection and diagnosis of Alzheimer disease.

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  9 in total

1.  Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.

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2.  Tissue segmentation: a crucial tool for quantitative MRI and visualization of anatomical structures.

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Journal:  MAGMA       Date:  2016-04       Impact factor: 2.310

3.  Structural alterations in patients with obsessive-compulsive disorder: a surface-based analysis of cortical volume, surface area and thickness.

Authors:  Oana Georgiana Rus; Tim Jonas Reess; Gerd Wagner; Michael Zaudig; Claus Zimmer; Kathrin Koch
Journal:  J Psychiatry Neurosci       Date:  2017-11       Impact factor: 6.186

Review 4.  Volumetric MRI Findings in Mild Traumatic Brain Injury (mTBI) and Neuropsychological Outcome.

Authors:  Erin D Bigler
Journal:  Neuropsychol Rev       Date:  2021-03-03       Impact factor: 7.444

5.  The Strengths and Obstacles in the Differential Diagnosis of Progressive Supranuclear Palsy-Parkinsonism Predominant (PSP-P) and Multiple System Atrophy (MSA) Using Magnetic Resonance Imaging (MRI) and Perfusion Single Photon Emission Computed Tomography (SPECT).

Authors:  Piotr Alster; Michał Nieciecki; Bartosz Migda; Michał Kutyłowski; Natalia Madetko; Karolina Duszyńska-Wąs; Ingeborga Charzyńska; Dariusz Koziorowski; Leszek Królicki; Andrzej Friedman
Journal:  Diagnostics (Basel)       Date:  2022-02-02

6.  A scalable method to improve gray matter segmentation at ultra high field MRI.

Authors:  Omer Faruk Gulban; Marian Schneider; Ingo Marquardt; Roy A M Haast; Federico De Martino
Journal:  PLoS One       Date:  2018-06-06       Impact factor: 3.240

7.  Presenting Symptoms and Disease Severity in Multiple Sclerosis Patients.

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8.  Towards an Architecture of a Multi-purpose, User-Extendable Reference Human Brain Atlas.

Authors:  Wieslaw L Nowinski
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9.  Validity of automated FreeSurfer segmentation compared to manual tracing in detecting prenatal alcohol exposure-related subcortical and corpus callosal alterations in 9- to 11-year-old children.

Authors:  Stevie C Biffen; Christopher M R Warton; Neil C Dodge; Christopher D Molteno; Joseph L Jacobson; Sandra W Jacobson; Ernesta M Meintjes
Journal:  Neuroimage Clin       Date:  2020-07-31       Impact factor: 4.881

  9 in total

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