Literature DB >> 27052370

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

Fritz Schick1.   

Abstract

Automatic or semi-automatic segmentation of tissue types or organs is well established for X-ray-based computed tomography, with its fixed grey-scale and tissue classes with well-established ranges of Hounsfield units. MRI is much more powerful with regard to soft tissue contrast and quantitative assessment of tissue properties (e.g., perfusion, diffusion, fat content), but the principle of signal generation and recording in MRI leads to inherent problems if simple threshold based segmentation procedures are applied. In this editorial in the special issue of MAGMA on tissue segmentation, a number of relevant methodical, scientific, and clinical aspects of reliable tissue segmentation using data recording by MRI are reported and discussed.

Keywords:  MRI, image segmentation algorithms; MRI, tissue classes; MRI, tissue segmentation

Mesh:

Year:  2016        PMID: 27052370     DOI: 10.1007/s10334-016-0549-0

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


  33 in total

1.  Abdominal fat distribution and peripheral and hepatic insulin resistance in type 2 diabetes mellitus.

Authors:  Yoshinori Miyazaki; Leonard Glass; Curtis Triplitt; Estela Wajcberg; Lawrence J Mandarino; Ralph A DeFronzo
Journal:  Am J Physiol Endocrinol Metab       Date:  2002-12       Impact factor: 4.310

2.  Multi-atlas-based fully automatic segmentation of individual muscles in rat leg.

Authors:  Michael Sdika; Anne Tonson; Yann Le Fur; Patrick J Cozzone; David Bendahan
Journal:  MAGMA       Date:  2015-12-08       Impact factor: 2.310

Review 3.  Principles and methods for automatic and semi-automatic tissue segmentation in MRI data.

Authors:  Lei Wang; Teodora Chitiboi; Hans Meine; Matthias Günther; Horst K Hahn
Journal:  MAGMA       Date:  2016-01-11       Impact factor: 2.310

Review 4.  T1 Mapping: Basic Techniques and Clinical Applications.

Authors:  Andrew J Taylor; Michael Salerno; Rohan Dharmakumar; Michael Jerosch-Herold
Journal:  JACC Cardiovasc Imaging       Date:  2016-01

Review 5.  Three- and four-dimensional ultrasonography for the structural and functional evaluation of the fetal face.

Authors:  Asim Kurjak; Guillermo Azumendi; Wiku Andonotopo; Aida Salihagic-Kadic
Journal:  Am J Obstet Gynecol       Date:  2006-10-02       Impact factor: 8.661

Review 6.  Image quality with non-standard nuclides in PET.

Authors:  R Laforest; X Liu
Journal:  Q J Nucl Med Mol Imaging       Date:  2007-11-28       Impact factor: 2.346

7.  Automated segmentation and volumetric analysis of renal cortex, medulla, and pelvis based on non-contrast-enhanced T1- and T2-weighted MR images.

Authors:  Susanne Will; Petros Martirosian; Christian Würslin; Fritz Schick
Journal:  MAGMA       Date:  2014-01-30       Impact factor: 2.310

8.  Potential influence of Gadolinium contrast on image segmentation in MR-based attenuation correction with Dixon sequences in whole-body 18F-FDG PET/MR.

Authors:  Verena Ruhlmann; Philipp Heusch; Hilmar Kühl; Karsten Beiderwellen; Gerald Antoch; Michael Forsting; Andreas Bockisch; Christian Buchbender; Harald H Quick
Journal:  MAGMA       Date:  2015-12-14       Impact factor: 2.310

Review 9.  Relevance of magnetic resonance imaging for early detection and diagnosis of Alzheimer disease.

Authors:  Stefan J Teipel; Michel Grothe; Simone Lista; Nicola Toschi; Francesco G Garaci; Harald Hampel
Journal:  Med Clin North Am       Date:  2013-02-01       Impact factor: 5.456

10.  Automatic anatomical brain MRI segmentation combining label propagation and decision fusion.

Authors:  Rolf A Heckemann; Joseph V Hajnal; Paul Aljabar; Daniel Rueckert; Alexander Hammers
Journal:  Neuroimage       Date:  2006-07-24       Impact factor: 6.556

View more
  5 in total

1.  Are Magnetic Resonance Imaging Technologies Crucial to Our Understanding of Spinal Conditions?

Authors:  Rebecca J Crawford; Maryse Fortin; Kenneth A Weber; Andrew Smith; James M Elliott
Journal:  J Orthop Sports Phys Ther       Date:  2019-03-26       Impact factor: 4.751

2.  Evaluation of an automated thresholding algorithm for the quantification of paraspinal muscle composition from MRI images.

Authors:  Maryse Fortin; Mona Omidyeganeh; Michele Crites Battié; Omair Ahmad; Hassan Rivaz
Journal:  Biomed Eng Online       Date:  2017-05-22       Impact factor: 2.819

3.  Semi-automated segmentation of magnetic resonance images for thigh skeletal muscle and fat using threshold technique after spinal cord injury.

Authors:  Mina P Ghatas; Robert M Lester; M Rehan Khan; Ashraf S Gorgey
Journal:  Neural Regen Res       Date:  2018-10       Impact factor: 5.135

4.  Automatic classification of tissues on pelvic MRI based on relaxation times and support vector machine.

Authors:  Jorge Arturo Zavala Bojorquez; Pierre-Marc Jodoin; Stéphanie Bricq; Paul Michael Walker; François Brunotte; Alain Lalande
Journal:  PLoS One       Date:  2019-02-22       Impact factor: 3.240

5.  Novel stochastic framework for automatic segmentation of human thigh MRI volumes and its applications in spinal cord injured individuals.

Authors:  Samineh Mesbah; Ahmed M Shalaby; Sean Stills; Ahmed M Soliman; Andrea Willhite; Susan J Harkema; Enrico Rejc; Ayman S El-Baz
Journal:  PLoS One       Date:  2019-05-09       Impact factor: 3.240

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.