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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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    Fig 1.

    Example of the deformation field inside a new lesion. All arrows point to the lesion center.

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    Fig 2.

    Example of the deformation field for 2 sections. The first image does not contain lesions and presents large deformations with no clear sinking patterns, while in the second image with a lesion, all the arrows inside the lesion point to the center.

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    Fig 3.

    New lesion detection. For each row, the first image is the baseline image, the second is the follow-up image, the third is the subtraction, and the fourth is the lesion analysis over the follow-up image (green = true-positive). The patient has a large number of TPs (100%), with a small number of FPs (0%).

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    Fig 4.

    Correlation between the number of ground truth lesions and the number of automatically detected ones (Pearson coefficient = 0.81, P = 2.2688e-09).

Tables

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    Table 1:

    Lesion detections obtained for our data base using various approaches

    ImageMethodASDTPfFPfDSC (Lesions)DSC (Volume)
    PDThreshold25.8092.2893.180.110.31
    Intensity rules2021.9080.6183.010.240.35
    DF19.9173.1877.020.300.37
    T2Threshold25.2293.8995.880.070.25
    Intensity rules2022.2264.0986.350.170.25
    DF17.7681.7980.840.260.34
    T2-FLAIRThreshold27.2290.2492.790.100.26
    Intensity rules2021.1778.3480.770.250.31
    DF21.1481.2277.110.300.33
    CombinationThreshold13.0791.0585.610.220.45
    Intensity rules2030.8051.6235.870.460.37
    Proposal7.8970.9317.800.680.52
    • Note:—ASD indicates average surface distance.

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    Table 2:

    Permutation test ranking of DSC values for the approaches applied on each image separatelya

    MethodMean P Value
    Rank 1 (<1 σ)T2-FLAIR-DF.75
    PD-DF.56
    T2-DF.53
    Rank 2 (<2 σ)T2-FLAIR20.22
    PD20.16
    Rank 3 (<3 σ)T220−.22
    PD-threshold−.56
    T2-FLAIR-threshold−.67
    T2-threshold−.78
    • ↵a Methods were ranked relative to the mean and SD of the method with the highest DSC value. Methods in the same rank have similar results, whereas methods in different ranks show significant differences.

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    Table 3:

    Analysis of the TPf before and after postprocessing with deformation fields for different sizesa

    ImageMethod3–1011–5050+
    CombinationCombination (threshold)71.4372.3895.16
    Proposal42.8648.5777.42
    • ↵a Lesions between 3 and 10 voxels are considered small; lesions between 11 and 50 voxels, medium; and lesions with >50 voxels, large.

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American Journal of Neuroradiology: 37 (10)
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M. Cabezas, J.F. Corral, A. Oliver, Y. Díez, M. Tintoré, C. Auger, X. Montalban, X. Lladó, D. Pareto, À. Rovira
Improved Automatic Detection of New T2 Lesions in Multiple Sclerosis Using Deformation Fields
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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