Literature DB >> 9621917

Automatic segmentation of gadolinium-enhanced multiple sclerosis lesions.

B J Bedell1, P A Narayana.   

Abstract

Automatic detection and quantitation of contrast-enhanced lesions on MRI is expected to be useful in characterizing the disease state in multiple sclerosis (MS). The enhancing structures such as cerebral vasculature and regions with no blood-brain barrier complicate automated analysis of lesion enhancement. A pulse sequence that incorporates both stationary and marching saturation bands and gradient dephasing is described for suppressing the enhancements within the cerebral vasculature. A postprocessing technique based on automatic image segmentation is implemented for eliminating enhancing structures such as choroid plexus. All MS lesions larger than 5 mm3 have been successfully identified. The automated analysis produced no false-positives or false-negative lesions above this volume in 13 patients with MS who were evaluated using the acquisition and evaluation techniques described.

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Year:  1998        PMID: 9621917     DOI: 10.1002/mrm.1910390611

Source DB:  PubMed          Journal:  Magn Reson Med        ISSN: 0740-3194            Impact factor:   4.668


  5 in total

1.  Automatic segmentation and volumetric quantification of white matter hyperintensities on fluid-attenuated inversion recovery images using the extreme value distribution.

Authors:  Rui Wang; Chao Li; Jie Wang; Xiaoer Wei; Yuehua Li; Yuemin Zhu; Su Zhang
Journal:  Neuroradiology       Date:  2014-11-19       Impact factor: 2.804

2.  Deep learning segmentation of gadolinium-enhancing lesions in multiple sclerosis.

Authors:  Ivan Coronado; Refaat E Gabr; Ponnada A Narayana
Journal:  Mult Scler       Date:  2020-05-22       Impact factor: 6.312

3.  Multiple sclerosis: identification of temporal changes in brain lesions with computer-assisted detection software.

Authors:  M Bilello; M Arkuszewski; P Nucifora; I Nasrallah; E R Melhem; L Cirillo; J Krejza
Journal:  Neuroradiol J       Date:  2013-05-10

Review 4.  Atlas-based neuroinformatics via MRI: harnessing information from past clinical cases and quantitative image analysis for patient care.

Authors:  Susumu Mori; Kenichi Oishi; Andreia V Faria; Michael I Miller
Journal:  Annu Rev Biomed Eng       Date:  2013-04-29       Impact factor: 9.590

5.  Increasing the contrast of the brain MR FLAIR images using fuzzy membership functions and structural similarity indices in order to segment MS lesions.

Authors:  Ahmad Bijar; Rasoul Khayati; Antonio Peñalver Benavent
Journal:  PLoS One       Date:  2013-06-17       Impact factor: 3.240

  5 in total

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