Literature DB >> 11721811

Multiprotocol MR image segmentation in multiple sclerosis: experience with over 1,000 studies.

J K Udupa1, L G Nyúl, Y Ge, R I Grossman.   

Abstract

RATIONALE AND
OBJECTIVES: Multiple sclerosis (MS) is an acquired disease of the central nervous system. Several clinical measures are commonly used to express the severity of the disease, including the Expanded Disability Status Scale and the ambulation index. These measures are subjective and may be difficult to reproduce. The aim of this research is to investigate the possibility of developing more objective measures derived from MR imaging.
MATERIALS AND METHODS: Various magnetic resonance (MR) imaging protocols are being investigated for the study of MS. Seeking to replace the Expanded Disability Status Scale and ambulation index with an objective means to assess the natural course of the disease and its response to therapy, the authors have developed multiprotocol MR image segmentation methods based on fuzzy connectedness to quantify both macrosopic features of the disease (lesions, gray matter, white matter, cerebrospinal fluid, and brain parenchyma) and the microscopic appearance of diseased white matter. Over 1,000 studies have been processed to date.
RESULTS: By far the strongest correlations with the clinical measures were demonstrated by the magnetization transfer ratio histogram parameters obtained for the various segmented tissue regions. These findings emphasize the importance of considering the microscopic and diffuse nature of the disease in the individual tissue regions. Brain parenchymal volume also demonstrated a strong correlation with clinical measures, which suggests that brain atrophy is an important disease indicator.
CONCLUSION: Fuzzy connectedness is a viable, highly reproducible segmentation method for studying MS.

Entities:  

Mesh:

Year:  2001        PMID: 11721811     DOI: 10.1016/S1076-6332(03)80723-7

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  9 in total

1.  Dirty-appearing white matter in multiple sclerosis: volumetric MR imaging and magnetization transfer ratio histogram analysis.

Authors:  Yulin Ge; Robert I Grossman; James S Babb; Juan He; Lois J Mannon
Journal:  AJNR Am J Neuroradiol       Date:  2003 Nov-Dec       Impact factor: 3.825

2.  Segmentation of electron tomographic data sets using fuzzy set theory principles.

Authors:  Edgar Garduño; Mona Wong-Barnum; Niels Volkmann; Mark H Ellisman
Journal:  J Struct Biol       Date:  2008-02-16       Impact factor: 2.867

Review 3.  An artificial immune-activated neural network applied to brain 3D MRI segmentation.

Authors:  Akmal Younis; Mohamed Ibrahim; Mansur Kabuka; Nigel John
Journal:  J Digit Imaging       Date:  2007-12-11       Impact factor: 4.056

4.  Parallel Fuzzy Segmentation of Multiple Objects.

Authors:  Edgar Garduño; Gabor T Herman
Journal:  Int J Imaging Syst Technol       Date:  2008       Impact factor: 2.000

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

Authors:  Zhiqiang Lao; Dinggang Shen; Dengfeng Liu; Abbas F Jawad; Elias R Melhem; Lenore J Launer; R Nick Bryan; Christos Davatzikos
Journal:  Acad Radiol       Date:  2008-03       Impact factor: 3.173

6.  Diminished visibility of cerebral venous vasculature in multiple sclerosis by susceptibility-weighted imaging at 3.0 Tesla.

Authors:  Yulin Ge; Vahe M Zohrabian; Etin-Osa Osa; Jian Xu; Hina Jaggi; Joseph Herbert; E Mark Haacke; Robert I Grossman
Journal:  J Magn Reson Imaging       Date:  2009-05       Impact factor: 4.813

7.  Reliability of tumor volume estimation from MR images in patients with malignant glioma. Results from the American College of Radiology Imaging Network (ACRIN) 6662 Trial.

Authors:  Birgit B Ertl-Wagner; Jeffrey D Blume; Donald Peck; Jayaram K Udupa; Benjamin Herman; Anthony Levering; Ilona M Schmalfuss
Journal:  Eur Radiol       Date:  2008-10-17       Impact factor: 5.315

Review 8.  A review of structural magnetic resonance neuroimaging.

Authors:  M Symms; H R Jäger; K Schmierer; T A Yousry
Journal:  J Neurol Neurosurg Psychiatry       Date:  2004-09       Impact factor: 10.154

9.  Feature-based fuzzy connectedness segmentation of ultrasound images with an object completion step.

Authors:  Sylvia Rueda; Caroline L Knight; Aris T Papageorghiou; J Alison Noble
Journal:  Med Image Anal       Date:  2015-07-17       Impact factor: 8.545

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.