Automated Detection of Lupus White Matter Lesions in MRI

Brain magnetic resonance imaging provides detailed information which can be used to detect and segment white matter lesions (WML). In this work we propose an approach to automatically segment WML in Lupus patients by using T1w and fluid-attenuated inversion recovery (FLAIR) images. Lupus WML appear...

Full description

Bibliographic Details
Main Authors:	Roura, Eloy, Sarbu, Nicolae, Oliver, Arnau, Valverde, Sergi, González-Villà, Sandra, Cervera, Ricard, Bargalló, Núria, Lladó, Xavier
Format:	Online
Language:	English
Published:	Frontiers Media S.A. 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4981618/

id	pubmed-4981618
recordtype	oai_dc
spelling	pubmed-49816182016-08-26 Automated Detection of Lupus White Matter Lesions in MRI Roura, Eloy Sarbu, Nicolae Oliver, Arnau Valverde, Sergi González-Villà, Sandra Cervera, Ricard Bargalló, Núria Lladó, Xavier Neuroscience Brain magnetic resonance imaging provides detailed information which can be used to detect and segment white matter lesions (WML). In this work we propose an approach to automatically segment WML in Lupus patients by using T1w and fluid-attenuated inversion recovery (FLAIR) images. Lupus WML appear as small focal abnormal tissue observed as hyperintensities in the FLAIR images. The quantification of these WML is a key factor for the stratification of lupus patients and therefore both lesion detection and segmentation play an important role. In our approach, the T1w image is first used to classify the three main tissues of the brain, white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF), while the FLAIR image is then used to detect focal WML as outliers of its GM intensity distribution. A set of post-processing steps based on lesion size, tissue neighborhood, and location are used to refine the lesion candidates. The proposal is evaluated on 20 patients, presenting qualitative, and quantitative results in terms of precision and sensitivity of lesion detection [True Positive Rate (62%) and Positive Prediction Value (80%), respectively] as well as segmentation accuracy [Dice Similarity Coefficient (72%)]. Obtained results illustrate the validity of the approach to automatically detect and segment lupus lesions. Besides, our approach is publicly available as a SPM8/12 toolbox extension with a simple parameter configuration. Frontiers Media S.A. 2016-08-12 /pmc/articles/PMC4981618/ /pubmed/27570507 http://dx.doi.org/10.3389/fninf.2016.00033 Text en Copyright © 2016 Roura, Sarbu, Oliver, Valverde, González-Villà, Cervera, Bargalló and Lladó. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Roura, Eloy Sarbu, Nicolae Oliver, Arnau Valverde, Sergi González-Villà, Sandra Cervera, Ricard Bargalló, Núria Lladó, Xavier
spellingShingle	Roura, Eloy Sarbu, Nicolae Oliver, Arnau Valverde, Sergi González-Villà, Sandra Cervera, Ricard Bargalló, Núria Lladó, Xavier Automated Detection of Lupus White Matter Lesions in MRI
author_facet	Roura, Eloy Sarbu, Nicolae Oliver, Arnau Valverde, Sergi González-Villà, Sandra Cervera, Ricard Bargalló, Núria Lladó, Xavier
author_sort	Roura, Eloy
title	Automated Detection of Lupus White Matter Lesions in MRI
title_short	Automated Detection of Lupus White Matter Lesions in MRI
title_full	Automated Detection of Lupus White Matter Lesions in MRI
title_fullStr	Automated Detection of Lupus White Matter Lesions in MRI
title_full_unstemmed	Automated Detection of Lupus White Matter Lesions in MRI
title_sort	automated detection of lupus white matter lesions in mri
description	Brain magnetic resonance imaging provides detailed information which can be used to detect and segment white matter lesions (WML). In this work we propose an approach to automatically segment WML in Lupus patients by using T1w and fluid-attenuated inversion recovery (FLAIR) images. Lupus WML appear as small focal abnormal tissue observed as hyperintensities in the FLAIR images. The quantification of these WML is a key factor for the stratification of lupus patients and therefore both lesion detection and segmentation play an important role. In our approach, the T1w image is first used to classify the three main tissues of the brain, white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF), while the FLAIR image is then used to detect focal WML as outliers of its GM intensity distribution. A set of post-processing steps based on lesion size, tissue neighborhood, and location are used to refine the lesion candidates. The proposal is evaluated on 20 patients, presenting qualitative, and quantitative results in terms of precision and sensitivity of lesion detection [True Positive Rate (62%) and Positive Prediction Value (80%), respectively] as well as segmentation accuracy [Dice Similarity Coefficient (72%)]. Obtained results illustrate the validity of the approach to automatically detect and segment lupus lesions. Besides, our approach is publicly available as a SPM8/12 toolbox extension with a simple parameter configuration.
publisher	Frontiers Media S.A.
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4981618/
_version_	1613626029876707328

Automated Detection of Lupus White Matter Lesions in MRI

Similar Items