Document Detail


Automated detection of multiple sclerosis lesions in serial brain MRI.
MedLine Citation:
PMID:  22179659     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
INTRODUCTION: Multiple sclerosis (MS) is a serious disease typically occurring in the brain whose diagnosis and efficacy of treatment monitoring are vital. Magnetic resonance imaging (MRI) is frequently used in serial brain imaging due to the rich and detailed information provided. METHODS: Time-series analysis of images is widely used for MS diagnosis and patient follow-up. However, conventional manual methods are time-consuming, subjective, and error-prone. Thus, the development of automated techniques for the detection and quantification of MS lesions is a major challenge. RESULTS: This paper presents an up-to-date review of the approaches which deal with the time-series analysis of brain MRI for detecting active MS lesions and quantifying lesion load change. We provide a comprehensive reference source for researchers in which several approaches to change detection and quantification of MS lesions are investigated and classified. We also analyze the results provided by the approaches, discuss open problems, and point out possible future trends. CONCLUSION: Lesion detection approaches are required for the detection of static lesions and for diagnostic purposes, while either quantification of detected lesions or change detection algorithms are needed to follow up MS patients. However, there is not yet a single approach that can emerge as a standard for the clinical practice, automatically providing an accurate MS lesion evolution quantification. Future trends will focus on combining the lesion detection in single studies with the analysis of the change detection in serial MRI.
Authors:
Xavier Lladó; Onur Ganiler; Arnau Oliver; Robert Martí; Jordi Freixenet; Laia Valls; Joan C Vilanova; Lluís Ramió-Torrentà; Alex Rovira
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-12-20
Journal Detail:
Title:  Neuroradiology     Volume:  -     ISSN:  1432-1920     ISO Abbreviation:  -     Publication Date:  2011 Dec 
Date Detail:
Created Date:  2011-12-19     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  1302751     Medline TA:  Neuroradiology     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
Computer Vision and Robotics Group, University of Girona, Edifici P-IV Campus de Montilivi s/n, 17071, Girona, Spain, llado@eia.udg.edu.
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