Paper
9 May 1997 Quantitative assessment of MS plaques and brain atrophy in multiple sclerosis using semiautomatic segmentation method
Tomi Heinonen, Prasun Dastidar, Pertti Ryymin, Antti J. Lahtinen, Hannu Eskola, Jaakko Malmivuo
Author Affiliations +
Abstract
Quantitative magnetic resonance (MR) imaging of the brain is useful in multiple sclerosis (MS) in order to obtain reliable indices of disease progression. The goal of this project was to estimate the total volume of gliotic and non gliotic plaques in chronic progressive multiple sclerosis with the help of a semiautomatic segmentation method developed at the Ragnar Granit Institute. Youth developed program running on a PC based computer provides de displays of the segmented data, in addition to the volumetric analyses. The volumetric accuracy of the program was demonstrated by segmenting MR images of fluid filed syringes. An anatomical atlas is to be incorporated in the segmentation system to estimate the distribution of MS plaques in various neural pathways of the brain. A total package including MS plaque volume estimation, estimation of brain atrophy and ventricular enlargement, distribution of MS plaques in different neural segments of the brain has ben planned for the near future. Our study confirmed that total lesion volumes in chronic MS disease show a poor correlation to EDSS scores but show a positive correlation to neuropsychological scores. Therefore accurate total volume measurements of MS plaques using the developed semiautomatic segmentation technique helped us to evaluate the degree of neuropsychological impairment.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tomi Heinonen, Prasun Dastidar, Pertti Ryymin, Antti J. Lahtinen, Hannu Eskola, and Jaakko Malmivuo "Quantitative assessment of MS plaques and brain atrophy in multiple sclerosis using semiautomatic segmentation method", Proc. SPIE 3033, Medical Imaging 1997: Physiology and Function from Multidimensional Images, (9 May 1997); https://doi.org/10.1117/12.274037
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KEYWORDS
Image segmentation

Magnetic resonance imaging

Brain

3D image processing

Neuroimaging

Tissues

Head

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