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Research ArticleSpine Imaging and Spine Image-Guided Interventions

Quantitative Analysis of Spinal Canal Areas in the Lumbar Spine: An Imaging Informatics and Machine Learning Study

B. Gaonkar, D. Villaroman, J. Beckett, C. Ahn, M. Attiah, D. Babayan, J.P. Villablanca, N. Salamon, A. Bui and L. Macyszyn
American Journal of Neuroradiology August 2019, DOI: https://doi.org/10.3174/ajnr.A6174
B. Gaonkar
aFrom the Departments of Neurosurgery (B.G., D.V., J.B., C.A., M.A., D.B., L.M.)
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  • ORCID record for B. Gaonkar
D. Villaroman
aFrom the Departments of Neurosurgery (B.G., D.V., J.B., C.A., M.A., D.B., L.M.)
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J. Beckett
aFrom the Departments of Neurosurgery (B.G., D.V., J.B., C.A., M.A., D.B., L.M.)
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C. Ahn
aFrom the Departments of Neurosurgery (B.G., D.V., J.B., C.A., M.A., D.B., L.M.)
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M. Attiah
aFrom the Departments of Neurosurgery (B.G., D.V., J.B., C.A., M.A., D.B., L.M.)
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D. Babayan
aFrom the Departments of Neurosurgery (B.G., D.V., J.B., C.A., M.A., D.B., L.M.)
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J.P. Villablanca
bRadiology (J.P.V., N.S., A.B., L.M.), University of California, Los Angeles, Los Angeles, California.
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N. Salamon
bRadiology (J.P.V., N.S., A.B., L.M.), University of California, Los Angeles, Los Angeles, California.
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A. Bui
bRadiology (J.P.V., N.S., A.B., L.M.), University of California, Los Angeles, Los Angeles, California.
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L. Macyszyn
aFrom the Departments of Neurosurgery (B.G., D.V., J.B., C.A., M.A., D.B., L.M.)
bRadiology (J.P.V., N.S., A.B., L.M.), University of California, Los Angeles, Los Angeles, California.
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  • Fig 1.
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    Fig 1.

    Variation of spinal canal area with level. A, This 3D model represents a generic lumbar spine where light blue objects represent an area of the central canal at each lumbar level at the midsection of a disc. The square frame (red) zooms in on the intervertebral disc (yellow) below L5 to give an axial view of where the central canal area (light blue) is located. In a randomly selected T2-MR imaging, each picture in this series B–F depicts 1 section of spinal cord segmentation (red) from each level. Tissues within the canal but outside the thecal sac are not segmented.

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    Fig 2.

    Sample case images of central canal segmentations. Three case images of axial T2 MR imaging (A) randomly selected from the dataset are shown alongside their resulting segmentations (blue) of the spinal canal using the proposed ensemble technique (B), segmentation (red) by manual rater 1 (C), and segmentation (green) by manual rater 2 (D).

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    Fig 3.

    Modes of segmentation failure of the proposed algorithm compared with U-Net results. Two scans using SVM + ERT failed (Dice score <0.7). The mode of failure was complete lack of segmentation as seen in rows A and B, thereby making it easy to eliminate such cases automatically. In contrast U-Net failures are more subtle and can involve under and oversegmentation as shown in row C.

Tables

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    Table 1:

    Comparison of automated spinal canal segmentations in a validation dataset of 109 axial MRIsa

    CentralityAuto vs Rater 1Auto vs Rater 2Rater 1 vs Rater 2
    Dice ratioMean0.84 ± 0.080.83 ± 0.080.9 ± 0.05
    Median0.870.850.92
    Hausdorff distance (mm)Mean7.89 ± 9.429.41 ± 11.27.90 ± 9.62
    Median4.595.644.66
    Average surface distance (mm)Mean0.84 ± 0.080.83 ± 0.080.9 ± 0.05
    Median0.100.140.07
    • ↵a Data are means and medians.

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    Table 2:

    Age, gender, and height were analyzed using a mixed effects model Canal Area ∼ age + gender + height + gender *height + (1 | subject). This table represents the Random Effects fita

    Random Effects
    GroupsNameVarianceSD
    Group(Intercept)286853.55
    Residual236548.63
    No. of obs8775No. of subjects1755
    • Note:—obs indicates observers.

    • ↵a The model fit is summarized. Height is the only variable that is statistically significantly related to canal areas.

    • View popup
    Table 3:

    Age, gender, and height were analyzed using a mixed effects model Canal Area ∼ age + gender + height + gender *height + (1 | subject). This table represents the Intercept fit - equivalent to traditional regressiona

    Fixed EffectsEstimateStandard ErrorT ValuePr (>|t|)
    (Intercept)−278.1849543.20594−6.4390.00
    Age0.051580.084290.6120.54
    Sex−0.6353962.56533−0.010.10
    Height8.000790.6618412.0890.00
    Sex:height−0.463110.93188−0.4970.62
    • Note:—Pr (>|t|) indicates p-value.

    • ↵a The model fit is summarized. Height is the only variable that is statistically significantly related to canal areas.

    • View popup
    Table 4:

    Age, gender, and height were analyzed using a mixed effects model Canal Area ∼ age + gender + height + gender *height + (1 | subject). This table represents the correlation of fixed effectsa

    Correlation of Fixed Effects(Intercept)AgeSexHeight
    Age−0.203
    Sex−0.6620.001
    Height−0.9950.110.672
    Sex:height0.692−0.009−0.998−0.703
    • ↵a The model fit is summarized. Height is the only variable that is statistically significantly related to canal areas.

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B. Gaonkar, D. Villaroman, J. Beckett, C. Ahn, M. Attiah, D. Babayan, J.P. Villablanca, N. Salamon, A. Bui, L. Macyszyn
Quantitative Analysis of Spinal Canal Areas in the Lumbar Spine: An Imaging Informatics and Machine Learning Study
American Journal of Neuroradiology Aug 2019, DOI: 10.3174/ajnr.A6174

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Quantitative Analysis of Spinal Canal Areas in the Lumbar Spine: An Imaging Informatics and Machine Learning Study
B. Gaonkar, D. Villaroman, J. Beckett, C. Ahn, M. Attiah, D. Babayan, J.P. Villablanca, N. Salamon, A. Bui, L. Macyszyn
American Journal of Neuroradiology Aug 2019, DOI: 10.3174/ajnr.A6174
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