Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning

Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning

Background: Identifying active lesions in magnetic resonance imaging (MRI) is crucial for the diagnosis and treatment planning of multiple sclerosis (MS). Active lesions on MRI are identified following the administration of Gadolinium-based contrast agents (GBCAs). However, recent studies have repor...

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Bibliographic Details
Main Authors: Jason Uwaeze, Ponnada A. Narayana, Arash Kamali, Vladimir Braverman, Michael A. Jacobs, Alireza Akhbardeh
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:Diagnostics
Subjects:
multiple sclerosis
dimensionality reduction
multiparametric MRI
lesion segmentation
Online Access:https://www.mdpi.com/2075-4418/14/6/632

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https://www.mdpi.com/2075-4418/14/6/632

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