Literature DB >> 8825269

Semi-automated thresholding technique for measuring lesion volumes in multiple sclerosis: effects of the change of the threshold on the computed lesion loads.

M Filippi1, M Rovaris, A Campi, C Pereira, G Comi.   

Abstract

Quantitative evaluation of lesion load in multiple sclerosis (MS) from magnetic resonance imaging (MRI) scans is becoming important in understanding and monitoring the progression of the disease. Methods of MS lesion segmentation based on intensity thresholding offer one of the most robust and easily-implemented means of computing the total lesion volume. This study evaluated the effects of slight changes in the choice of intensity threshold on computed lesion volumes in 20 patients with MS using such a technique. After judging the optimum choice of threshold value, the threshold value was increased and decreased by 3% in 1% steps around this value; we observed a mean change of 15% in computed lesion volumes for 1% changes of threshold value. Larger changes in lesion volume were found when the threshold was changed by larger amounts. On the other hand, the amount of time required for manual review decreased, and the confidence with which manual review could be performed increased when using lower thresholds. This study shows that the choice of threshold is a crucial factor in measuring lesion volumes in MS when using intensity-based techniques. It also suggests that in multicenter and/or longitudinal studies, criteria for choosing the threshold should be developed whereby the threshold level should be set such that all MR visible lesions are above it, in order to minimise the human interaction and, consequently, the reproducibility of the results.

Entities:  

Mesh:

Year:  1996        PMID: 8825269     DOI: 10.1111/j.1600-0404.1996.tb00166.x

Source DB:  PubMed          Journal:  Acta Neurol Scand        ISSN: 0001-6314            Impact factor:   3.209


  6 in total

1.  Computerised volumetric analysis of lesions in multiple sclerosis using new semi-automatic segmentation software.

Authors:  P Dastidar; T Heinonen; T Vahvelainen; I Elovaara; H Eskola
Journal:  Med Biol Eng Comput       Date:  1999-01       Impact factor: 2.602

Review 2.  Magnetic resonance in monitoring the natural history of multiple sclerosis and the effects of treatment.

Authors:  M Filippi; M Rovaris; G Comi
Journal:  Ital J Neurol Sci       Date:  1996-12

Review 3.  Classification of white matter lesions on magnetic resonance imaging in elderly persons.

Authors:  Ki Woong Kim; James R MacFall; Martha E Payne
Journal:  Biol Psychiatry       Date:  2008-05-08       Impact factor: 13.382

Review 4.  Automatic Detection of White Matter Hyperintensities in Healthy Aging and Pathology Using Magnetic Resonance Imaging: A Review.

Authors:  Maria Eugenia Caligiuri; Paolo Perrotta; Antonio Augimeri; Federico Rocca; Aldo Quattrone; Andrea Cherubini
Journal:  Neuroinformatics       Date:  2015-07

5.  A dual modeling approach to automatic segmentation of cerebral T2 hyperintensities and T1 black holes in multiple sclerosis.

Authors:  Alessandra M Valcarcel; Kristin A Linn; Fariha Khalid; Simon N Vandekar; Shahamat Tauhid; Theodore D Satterthwaite; John Muschelli; Melissa Lynne Martin; Rohit Bakshi; Russell T Shinohara
Journal:  Neuroimage Clin       Date:  2018-10-16       Impact factor: 4.881

6.  Robust, atlas-free, automatic segmentation of brain MRI in health and disease.

Authors:  Kartiga Selvaganesan; Emily Whitehead; Paba M DeAlwis; Matthew K Schindler; Souheil Inati; Ziad S Saad; Joan E Ohayon; Irene C M Cortese; Bryan Smith; Avindra Nath; Daniel S Reich; Sara Inati; Govind Nair
Journal:  Heliyon       Date:  2019-02-18
  6 in total

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