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Research ArticleSpine Imaging and Spine Image-Guided Interventions
Open Access

Automatic Spinal Cord Gray Matter Quantification: A Novel Approach

C. Tsagkas, A. Horvath, A. Altermatt, S. Pezold, M. Weigel, T. Haas, M. Amann, L. Kappos, T. Sprenger, O. Bieri, P. Cattin and K. Parmar
American Journal of Neuroradiology September 2019, 40 (9) 1592-1600; DOI: https://doi.org/10.3174/ajnr.A6157
C. Tsagkas
aFrom the Neurologic Clinic and Policlinic (C.T., M.A., L.K., T.S., K.P.), Department of Medicine and Biomedical Engineering
bTranslational Imaging in Neurology Basel (C.T., A.A., M.A., M.W., L.K., K.P.), Department of Medicine and Biomedical Engineering
eMedical Image Analysis Center (C.T., A.A., M.A.), Basel, Switzerland
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A. Horvath
fDepartment of Biomedical Engineering (A.H., A.A., S.P., M.W., O.B., P.C.), University of Basel, Allschwil, Switzerland
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A. Altermatt
bTranslational Imaging in Neurology Basel (C.T., A.A., M.A., M.W., L.K., K.P.), Department of Medicine and Biomedical Engineering
eMedical Image Analysis Center (C.T., A.A., M.A.), Basel, Switzerland
fDepartment of Biomedical Engineering (A.H., A.A., S.P., M.W., O.B., P.C.), University of Basel, Allschwil, Switzerland
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S. Pezold
fDepartment of Biomedical Engineering (A.H., A.A., S.P., M.W., O.B., P.C.), University of Basel, Allschwil, Switzerland
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M. Weigel
bTranslational Imaging in Neurology Basel (C.T., A.A., M.A., M.W., L.K., K.P.), Department of Medicine and Biomedical Engineering
cDivision of Radiological Physics (M.W., T.H., O.B.), Department of Radiology
fDepartment of Biomedical Engineering (A.H., A.A., S.P., M.W., O.B., P.C.), University of Basel, Allschwil, Switzerland
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T. Haas
cDivision of Radiological Physics (M.W., T.H., O.B.), Department of Radiology
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M. Amann
aFrom the Neurologic Clinic and Policlinic (C.T., M.A., L.K., T.S., K.P.), Department of Medicine and Biomedical Engineering
bTranslational Imaging in Neurology Basel (C.T., A.A., M.A., M.W., L.K., K.P.), Department of Medicine and Biomedical Engineering
dDivision of Diagnostic and Interventional Neuroradiology (M.A.), Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland
eMedical Image Analysis Center (C.T., A.A., M.A.), Basel, Switzerland
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L. Kappos
aFrom the Neurologic Clinic and Policlinic (C.T., M.A., L.K., T.S., K.P.), Department of Medicine and Biomedical Engineering
bTranslational Imaging in Neurology Basel (C.T., A.A., M.A., M.W., L.K., K.P.), Department of Medicine and Biomedical Engineering
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T. Sprenger
aFrom the Neurologic Clinic and Policlinic (C.T., M.A., L.K., T.S., K.P.), Department of Medicine and Biomedical Engineering
gDepartment of Neurology (T.S.), DKD HELIOS Klinik, Wiesbaden, Germany.
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O. Bieri
cDivision of Radiological Physics (M.W., T.H., O.B.), Department of Radiology
fDepartment of Biomedical Engineering (A.H., A.A., S.P., M.W., O.B., P.C.), University of Basel, Allschwil, Switzerland
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P. Cattin
fDepartment of Biomedical Engineering (A.H., A.A., S.P., M.W., O.B., P.C.), University of Basel, Allschwil, Switzerland
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K. Parmar
aFrom the Neurologic Clinic and Policlinic (C.T., M.A., L.K., T.S., K.P.), Department of Medicine and Biomedical Engineering
bTranslational Imaging in Neurology Basel (C.T., A.A., M.A., M.W., L.K., K.P.), Department of Medicine and Biomedical Engineering
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  • Fig 1.
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    Fig 1.

    Exemplary axial AMIRA slice of 1 representative volunteer at the C4 level. A–H, Eight images of different tissue contrast acquired by the AMIRA sequence, shown in chronologic order from lowest-to-highest TI. I, Average image from A to E in full view, which delivers a high contrast-to-noise-ratio for GM/WM. J, Average image from F to H, which delivers a high contrast-to-noise ratio for SC/CSF. K, Same average image as in I but histogram-equalized and zoomed.

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    Fig 2.

    Flow chart of the automatic segmentation pipeline. As a first step of the algorithm to align the 12 slices, the images are center-cropped and slice-wise successively coregistered rostral to caudal using translations in pixel-size steps to prevent further interpolation. Then, the algorithm automatically locates and delineates the ring-shaped CSF from its surroundings and extracts the cross-sectional SC surface. Finally, it uses the previously segmented SC surface as a mask for GM/WM differentiation. The iterative steps of CSF segmentation are shown as a zoomed-in view. GM segmentation uses essentially the same steps and is thus not shown in detail.

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    Fig 3.

    Cross-sectional areas of total spinal cord, white matter, and gray matter per axial slice as measured by automatic segmentations. Notice the slight increase of total spinal cord (TSC) and the marked GM cross-sectional area increase caudally, which corresponds to the cervical SC enlargement. The light gray area depicts the limits of ±1 SD.

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    Fig 4.

    Comparison between the reproducibility of manual and automatic measurements (AM) of spinal cord gray matter and white matter per axial slice. Intrasession and intersession reproducibility is assessed in terms of Dice coefficients (graphics on the left) and coefficients of variation (graphics on the right). Manual and automatic intersession reproducibility is shown in dark gray, whereas manual and automatic intrasession reproducibility is shown in light gray. Error bars display mean values ± 0.2 SDs.

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    Fig 5.

    Accuracy measurements in terms of Dice coefficients (graphics on the left) and Hausdorff distances (graphics on the right) of white matter and gray matter per slice. Overlaid boxplots display median values as well as 25th and 75th percentile values. Gray areas depict the mean standard error values ± 1 SD.

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    Fig 6.

    Examples of segmentations of representative patients with MS. The thick continuous line indicates automatic segmentation; the dashed line, manual reference standard. A, A 54-year-old female patient with MS. Rostral cervical SC slices of the C1/C2 level without focal lesions. Automatic segmentation highly corresponds to the manual reference standard. B, A 32-year-old male patient with MS. Rostral cervical SC slice of the C2 level with a focal posterolateral lesion fused with the left posterior gray matter horn. Automatic segmentation misclassifies the focal lesion as SC GM. C, A 33-year-old female patient with MS. A cervical SC slice of the C3/C4 level with a focal posterior lesion fusing with the posterior SC GM horns and the central SC GM commissure. Automatic segmentation misclassifies the focal lesion as SC GM and CSF.

Tables

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  • SC GM, WM, and TSC accuracy of automatic and semiautomatic segmentations against the manual reference standard segmentationsa

    ParameterGMWMTSC
    AM (on 88% of acquired slices, nondiscarded from initial analysis)DSC0.86 ± 0.04 (0.87)0.90 ± 0.03 (0.91)0.95 ± 0.03 (0.95)
    HD (mm)0.90 ± 0.44 (0.72)0.82 ± 0.22 (0.75)0.64 ± 0.27 (0.67)
    SAM (on 12% of acquired slices, discarded from initial analysis)DSC0.83 ± 0.04 (0.84)0.96 ± 0.01 (0.96)
    HD (mm)1.11 ± 0.55 (0.93)0.64 ± 0.15 (0.67)
    AM (on nondiscarded samples) and SAM (on discarded slices), mixed (100% of acquired slices)DSC0.86 ± 0.04 (0.86)0.91 ± 0.04 (0.92)0.96 ± 0.03 (0.96)
    HD (mm)0.91 ± 0.46 (0.81)0.80 ± 0.22 (0.75)0.60 ± 0.29 (0.55)
    • Note:—SAM indicates semiautomatic segmentation; TSC, total spinal cord; AM, automatic segmentations; DSC, Dice coefficient; HD, Hausdorff distances.

    • ↵a All values are shown as mean ± SD (median).

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American Journal of Neuroradiology: 40 (9)
American Journal of Neuroradiology
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C. Tsagkas, A. Horvath, A. Altermatt, S. Pezold, M. Weigel, T. Haas, M. Amann, L. Kappos, T. Sprenger, O. Bieri, P. Cattin, K. Parmar
Automatic Spinal Cord Gray Matter Quantification: A Novel Approach
American Journal of Neuroradiology Sep 2019, 40 (9) 1592-1600; DOI: 10.3174/ajnr.A6157

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Automatic Spinal Cord Gray Matter Quantification: A Novel Approach
C. Tsagkas, A. Horvath, A. Altermatt, S. Pezold, M. Weigel, T. Haas, M. Amann, L. Kappos, T. Sprenger, O. Bieri, P. Cattin, K. Parmar
American Journal of Neuroradiology Sep 2019, 40 (9) 1592-1600; DOI: 10.3174/ajnr.A6157
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