Identification of Multiple Sclerosis lesion subtypes and their quantitative assessments with EDSS using neuroimaging

Karami, Vania; Khayati, Rasoul; Habibzadeh, Amir; Nabavi, Seyed Massood

doi:10.1007/s13721-020-00245-8

Cited by 1 publication

(2 citation statements)

References 26 publications

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“…Since the grey matter was within the border of the brain mask and in conjunction with the background, filtering the background in this method might be conducive to some false‐positive segmented pixel in grey matter tissues. A post‐processing procedure was implemented based on applying simultaneous gradient and variance windows to reduce the false‐positive results [40]. A 3 × 3 × 3 cubic window was moved onto FLAIR slices to calculate the corresponding gradient and variance features for each pixel.…”

Section: Methodsmentioning

confidence: 99%

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Perfusion MRI in automatic classification of multiple sclerosis lesion subtypes

Homayouny

Khayati

Nabavi

et al. 2022

IET Signal Processing

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This retrospective and exploratory study investigated the efficiency of the 3T perfusion magnetic resonance imaging (MRI) at the classification of MS lesion subtypes. For the MS lesion subtype classification, firstly, it was necessary to segment all MS lesions. Therefore, a Bayesian classifier based on the adaptive mixture method was used to segment all lesions, and an artificial neural network (ANN) employed a multi‐layer Perceptron as a subtype classifier. The Bayesian classifier accomplished the segmentation of lesions using Fluid Attenuated Inversion Recovery automatically, and the ANN part was used as a subtype classifier that worked based on extracted information from perfusion MRI (i.e. Mean Transit Time and Cerebral Blood Volume maps) along with the intensity information of the conventional multi‐channel MRI in segmented lesions. Adding 3‐Tesla perfusion MRI to the proposed model for the subtype classification led to an increment of about 7% and 13% in the sensitivity of acute and chronic lesion classifications, respectively. The sensitivity of T2 lesions did not meaningfully change. The overall accuracy of the classification for acute, chronic, and T2 lesion classifications was 96.1%, 90.5%, and 92.9%, respectively. The proposed architectures reached high sensitivity in discrimination between MS lesion subtypes when 3T perfusion MRIs were used.

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Section: Methodsmentioning

confidence: 99%

“…(d) Grey matter border extraction according to Ref. [40]. (e) Formation of binary lesion mask using (c) and (d) to preclude the false Positives.…”

Section: Methodsmentioning

confidence: 99%