Literature DB >> 11358659

Increased differentiation of intracranial white matter lesions by multispectral 3D-tissue segmentation: preliminary results.

F B Mohamed1, S Vinitski, C F Gonzalez, S H Faro, F A Lublin, R Knobler, J E Gutierrez.   

Abstract

MRI is a very sensitive imaging modality, however with relatively low specificity. The aim of this work was to determine the potential of image post-processing using 3D-tissue segmentation technique for identification and quantitative characterization of intracranial lesions primarily in the white matter. Forty subjects participated in this study: 28 patients with brain multiple sclerosis (MS), 6 patients with subcortical ischemic vascular dementia (SIVD), and 6 patients with lacunar white matter infarcts (LI). In routine MR imaging these pathologies may be almost indistinguishable. The 3D-tissue segmentation technique used in this study was based on three input MR images (T(1), T(2)-weighted, and proton density). A modified k-Nearest-Neighbor (k-NN) algorithm optimized for maximum computation speed and high quality segmentation was utilized. In MS lesions, two very distinct subsets were classified using this procedure. Based on the results of segmentation one subset probably represent gliosis, and the other edema and demyelination. In SIVD, the segmented images demonstrated homogeneity, which differentiates SIVD from the heterogeneity observed in MS. This homogeneity was in agreement with the general histological findings. The LI changes pathophysiologically from subacute to chronic. The segmented images closely correlated with these changes, showing a central area of necrosis with cyst formation surrounded by an area that appears like reactive gliosis. In the chronic state, the cyst intensity was similar to that of CSF, while in the subacute stage, the peripheral rim was more prominent. Regional brain lesion load were also obtained on one MS patient to demonstrate the potential use of this technique for lesion load measurements. The majority of lesions were identified in the parietal and occipital lobes. The follow-up study showed qualitatively and quantitatively that the calculated MS load increase was associated with brain atrophy represented by an increase in CSF volume as well as decrease in "normal" brain tissue volumes. Importantly, these results were consistent with the patient's clinical evolution of the disease after a six-month period. In conclusion, these results show there is a potential application for a 3D tissue segmentation technique to characterize white matter lesions with similar intensities on T(2)-weighted MR images. The proposed methodology warrants further clinical investigation and evaluation in a large patient population.

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Year:  2001        PMID: 11358659     DOI: 10.1016/s0730-725x(01)00291-0

Source DB:  PubMed          Journal:  Magn Reson Imaging        ISSN: 0730-725X            Impact factor:   2.546


  9 in total

Review 1.  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

2.  Application of variable threshold intensity to segmentation for white matter hyperintensities in fluid attenuated inversion recovery magnetic resonance images.

Authors:  Byung Il Yoo; Jung Jae Lee; Ji Won Han; San Yeo Wool Oh; Eun Young Lee; James R MacFall; Martha E Payne; Tae Hui Kim; Jae Hyoung Kim; Ki Woong Kim
Journal:  Neuroradiology       Date:  2014-02-04       Impact factor: 2.804

Review 3.  Segmentation of multiple sclerosis lesions in MR images: a review.

Authors:  Daryoush Mortazavi; Abbas Z Kouzani; Hamid Soltanian-Zadeh
Journal:  Neuroradiology       Date:  2011-05-17       Impact factor: 2.804

4.  Improving Multiple Sclerosis Plaque Detection Using a Semiautomated Assistive Approach.

Authors:  J van Heerden; D Rawlinson; A M Zhang; R Chakravorty; M A Tacey; P M Desmond; F Gaillard
Journal:  AJNR Am J Neuroradiol       Date:  2015-06-18       Impact factor: 3.825

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

Review 6.  Age-related small vessel disease: a potential contributor to neurodegeneration in multiple sclerosis.

Authors:  Ruth Geraldes; Margaret M Esiri; Gabriele C DeLuca; Jacqueline Palace
Journal:  Brain Pathol       Date:  2017-03-12       Impact factor: 6.508

7.  Multiple Sclerosis Lesions Segmentation in Magnetic Resonance Imaging using Ensemble Support Vector Machine (ESVM).

Authors:  S HosseiniPanah; A Zamani; F Emadi; F HamtaeiPour
Journal:  J Biomed Phys Eng       Date:  2019-12-01

Review 8.  Recommendations to improve imaging and analysis of brain lesion load and atrophy in longitudinal studies of multiple sclerosis.

Authors:  H Vrenken; M Jenkinson; M A Horsfield; M Battaglini; R A van Schijndel; E Rostrup; J J G Geurts; E Fisher; A Zijdenbos; J Ashburner; D H Miller; M Filippi; F Fazekas; M Rovaris; A Rovira; F Barkhof; N de Stefano
Journal:  J Neurol       Date:  2012-12-21       Impact factor: 4.849

9.  Automated Detection of Lupus White Matter Lesions in MRI.

Authors:  Eloy Roura; Nicolae Sarbu; Arnau Oliver; Sergi Valverde; Sandra González-Villà; Ricard Cervera; Núria Bargalló; Xavier Lladó
Journal:  Front Neuroinform       Date:  2016-08-12       Impact factor: 4.081

  9 in total

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