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Research ArticleADULT BRAIN

Improved Automatic Detection of New T2 Lesions in Multiple Sclerosis Using Deformation Fields

M. Cabezas, J.F. Corral, A. Oliver, Y. Díez, M. Tintoré, C. Auger, X. Montalban, X. Lladó, D. Pareto and À. Rovira
American Journal of Neuroradiology October 2016, 37 (10) 1816-1823; DOI: https://doi.org/10.3174/ajnr.A4829
M. Cabezas
aFrom the Section of Neuroradiology, Department of Radiology (M.C., J.F.C., C.A., D.P., À.R.)
cVisió per Computador i Robòtica group (M.C., A.O., Y.D., X.L.), University of Girona, Girona, Spain.
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J.F. Corral
aFrom the Section of Neuroradiology, Department of Radiology (M.C., J.F.C., C.A., D.P., À.R.)
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A. Oliver
cVisió per Computador i Robòtica group (M.C., A.O., Y.D., X.L.), University of Girona, Girona, Spain.
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Y. Díez
cVisió per Computador i Robòtica group (M.C., A.O., Y.D., X.L.), University of Girona, Girona, Spain.
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M. Tintoré
bCentre d'Esclerosi Múltiple de Catalunya, Department of Neurology/Neuroimmunology (M.T., X.M.), Vall d'Hebron University Hospital, Vall d'Hebron Research Institute, Autonomous University of Barcelona, Barcelona, Spain
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C. Auger
aFrom the Section of Neuroradiology, Department of Radiology (M.C., J.F.C., C.A., D.P., À.R.)
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X. Montalban
bCentre d'Esclerosi Múltiple de Catalunya, Department of Neurology/Neuroimmunology (M.T., X.M.), Vall d'Hebron University Hospital, Vall d'Hebron Research Institute, Autonomous University of Barcelona, Barcelona, Spain
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X. Lladó
cVisió per Computador i Robòtica group (M.C., A.O., Y.D., X.L.), University of Girona, Girona, Spain.
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D. Pareto
aFrom the Section of Neuroradiology, Department of Radiology (M.C., J.F.C., C.A., D.P., À.R.)
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À. Rovira
aFrom the Section of Neuroradiology, Department of Radiology (M.C., J.F.C., C.A., D.P., À.R.)
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Abstract

BACKGROUND AND PURPOSE: Detection of disease activity, defined as new/enlarging T2 lesions on brain MR imaging, has been proposed as a biomarker in MS. However, detection of new/enlarging T2 lesions can be hindered by several factors that can be overcome with image subtraction. The purpose of this study was to improve automated detection of new T2 lesions and reduce user interaction to eliminate inter- and intraobserver variability.

MATERIALS AND METHODS: Multiparametric brain MR imaging was performed at 2 time points in 36 patients with new T2 lesions. Images were registered by using an affine transformation and the Demons algorithm to obtain a deformation field. After affine registration, images were subtracted and a threshold was applied to obtain a lesion mask, which was then refined by using the deformation field, intensity, and local information. This pipeline was compared with only applying a threshold, and with a state-of-the-art approach relying only on image intensities. To assess improvements, we compared the results of the different pipelines with the expert visual detection.

RESULTS: The multichannel pipeline based on the deformation field obtained a detection Dice similarity coefficient close to 0.70, with a false-positive detection of 17.8% and a true-positive detection of 70.9%. A statistically significant correlation (r = 0.81, P value = 2.2688e-09) was found between visual detection and automated detection by using our approach.

CONCLUSIONS: The deformation field–based approach proposed in this study for detecting new/enlarging T2 lesions resulted in significantly fewer false-positives while maintaining most true-positives and showed a good correlation with visual detection annotations. This approach could reduce user interaction and inter- and intraobserver variability.

ABBREVIATIONS:

BL
baseline
CIS
clinically isolated syndrome
DSC
Dice similarity coefficient
DF
deformation fields
FP
false-positive
FPf
false-positive fraction
FU
follow-up
PD
proton density
TP
true-positive
TPf
true-positive fraction
  • © 2016 by American Journal of Neuroradiology
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American Journal of Neuroradiology: 37 (10)
American Journal of Neuroradiology
Vol. 37, Issue 10
1 Oct 2016
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Improved Automatic Detection of New T2 Lesions in Multiple Sclerosis Using Deformation Fields
M. Cabezas, J.F. Corral, A. Oliver, Y. Díez, M. Tintoré, C. Auger, X. Montalban, X. Lladó, D. Pareto, À. Rovira
American Journal of Neuroradiology Oct 2016, 37 (10) 1816-1823; DOI: 10.3174/ajnr.A4829

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Improved Automatic Detection of New T2 Lesions in Multiple Sclerosis Using Deformation Fields
M. Cabezas, J.F. Corral, A. Oliver, Y. Díez, M. Tintoré, C. Auger, X. Montalban, X. Lladó, D. Pareto, À. Rovira
American Journal of Neuroradiology Oct 2016, 37 (10) 1816-1823; DOI: 10.3174/ajnr.A4829
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