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AJNR Awards, New Junior Editors, and more. Read the latest AJNR updates

Research ArticleSpine
Open Access

Deep Learning–Based Automatic Segmentation of Lumbosacral Nerves on CT for Spinal Intervention: A Translational Study

G. Fan, H. Liu, Z. Wu, Y. Li, C. Feng, D. Wang, J. Luo, W.M. Wells and S. He
American Journal of Neuroradiology June 2019, 40 (6) 1074-1081; DOI: https://doi.org/10.3174/ajnr.A6070
G. Fan
aFrom the Orthopedic Department, Shanghai Tenth People's Hospital (G.F., C.F., D.W., S.H.), Tongji University School of Medicine, Shanghai, China
bDepartment of Spine Surgery (G.F.), Third Affiliated Hospital of Sun Yatsen University, Guangzhou, China
cSurgical Planning Lab (G.F., J.L., W.M.W.), Radiology Department, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
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H. Liu
dSpinal Pain Research Institute of Tongji University (H.L., C.F., D.W., S.H.), Shanghai, China
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Z. Wu
eSchool of Data and Computer Science (Z.W.), Sun Yat-sen University, Guangzhou, China
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Y. Li
fShanghai Jiao Tong University School of Medicine (Y.L.), Shanghai, China
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C. Feng
aFrom the Orthopedic Department, Shanghai Tenth People's Hospital (G.F., C.F., D.W., S.H.), Tongji University School of Medicine, Shanghai, China
dSpinal Pain Research Institute of Tongji University (H.L., C.F., D.W., S.H.), Shanghai, China
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D. Wang
aFrom the Orthopedic Department, Shanghai Tenth People's Hospital (G.F., C.F., D.W., S.H.), Tongji University School of Medicine, Shanghai, China
dSpinal Pain Research Institute of Tongji University (H.L., C.F., D.W., S.H.), Shanghai, China
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J. Luo
cSurgical Planning Lab (G.F., J.L., W.M.W.), Radiology Department, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
gGraduate School of Frontier Sciences (J.L.), University of Tokyo, Tokyo, Japan.
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W.M. Wells III
cSurgical Planning Lab (G.F., J.L., W.M.W.), Radiology Department, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
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S. He
aFrom the Orthopedic Department, Shanghai Tenth People's Hospital (G.F., C.F., D.W., S.H.), Tongji University School of Medicine, Shanghai, China
dSpinal Pain Research Institute of Tongji University (H.L., C.F., D.W., S.H.), Shanghai, China
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Article Figures & Data

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  • Fig 1.
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    Fig 1.

    Manual segmentation and 3D reconstruction on Slicer. A, manual labels. B, 3D reconstruction with a coronal image. C, Illustrations of the Dice score, Intersection-over-Union, and pixel accuracy.

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    Fig 2.

    Schematic of the network architecture.

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    Fig 3.

    Measurement of safe and Kambin triangles. A, Schematics of the Kambin triangle. B, Schematics of the safe triangle. C, Measurement of the Kambin triangle on a manually segmented image. D, Measurement of the safe triangle on automatically segmented images.

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    Fig 4.

    Automatic and manually labeled masks.

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    Fig 5.

    3D rendering of automatic masks and manually labeled masks of bones and nerves. A, 3D rendering of manual masks before preprocessing. B, 3D rendering of post-preprocessed masks. C, 3D rendering of automatically generated masks. D, Smoothed 3D rendering of automatically generated masks. E, 3D rendering of manual masks before preprocessing (arrow indicates the compressed dura). F, 3D rendering of post-preprocessed masks (arrow indicates the compressed dura). G, 3D rendering of automatically generated masks (arrow indicates the compressed dura). H, Smoothed 3D rendering of automatically generated masks (arrow indicates the compressed dura).

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    Fig 6.

    3D model–based viability assessment of a transforaminal epidural steroid injection. A, Inaccessible trajectory to the safe triangle on an axial CT slice. B, Minimal space of the safe triangle on the posterior 3D model. C, Accessible oblique trajectory to the safe triangle on the 3D model. D, Inaccessible trajectory to the Kambin triangle on axial CT slice. E, Accessible trajectory on the 3D model. F, Oblique trajectory-guided nonaxial CT plane.

Tables

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    Table 1:

    Overview of the combined algorithm

    Algorithm 1: Combined Algorithm
    Require: X: CT volume, shape = D × H × W
    Require: xi=X(Li), (i=1, .…, k): CT voxel patch
    Require: yi = M(xi): yi is the output of the last layer (softmax activation function) of the model M, yi has 1 more dimension than xi, and this dimension has 3 channels. Each channel refers to the probability of the corresponding voxel belonging to background or bone or nerve, respectively.
    1) Initialize: Y ← 0
    2) For xi ∈ X,(i = 1, …, k) do
    3) Y(Li,:) + = yi
    4) End for
    5) S ← arg max(Y, axis = −1) (find the channel with the largest value in the last dimension)
    6) Return S (the automatic mask)
    • Note:—M indicates the model (network); L, location of the CT voxel patch x at the CT volumn X; Y, summed probability; max, maximum.

    • View popup
    Table 2:

    Segmentation accuracy in 10 testing casesa

    StructuresPixel Accuracy (%)IoU (%)Dice Score (%)
    Bones94.05 ± 6.6889.73 ± 4.3294.54 ± 2.43
    (82.0–99.9)(82.0–95.2)(90.1–97.5)
    Nerves91.43 ± 3.4882.71 ± 3.2590.51 ± 1.94
    (85.3–94.4)(76.3–87.4)(86.6–93.2)
    • ↵a Data are means and percentages unless otherwise noted.

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    Table 3:

    Segmentation accuracy in 10 testing cases from the open dataseta

    StructuresPixel Accuracy (%)IoU (%)Dice Score (%)
    Bones99.62 ± 0.3581.40 ± 11.3389.34 ± 7.28
    (99.3–99.9)(60.5–93.3)(75.42–96.5)
    Nerves87.74 ± 4.8280.64 ± 3.3189.25 ± 2.00
    (79.4–93.1)(75.5–82.9)(88.1–93.4)
    • ↵a Data are means and percentages unless otherwise noted.

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    Table 4:

    Measured area of the safe and Kambin trianglesa

    Area (mm2)Manual ImagesAutomatic ImagesP Value
    Kambin triangle37.80 ± 20.90 (15.11–87.51)36.41 ± 19.27 (11.46–78.63).302
    Safe triangle8.69 ± 2.24 (6.04–13.27)8.56 ± 3.25 (3.18–17.91).792
    • ↵a Data are means and percentages unless otherwise noted.

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    Table 5:

    Test-retest reliability and interobserver reliability of multiple measurements

    Intraclass Correlation CoefficientTest-Retest ReliabilityInterobserver Reliability
    3D rendering of manual segmentation
        Kambin triangle0.9830.984
        Safe triangle0.8810.922
    3D rendering of automatic segmentation
        Kambin triangle0.9880.982
        Safe triangle0.9770.959
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American Journal of Neuroradiology: 40 (6)
American Journal of Neuroradiology
Vol. 40, Issue 6
1 Jun 2019
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Cite this article
G. Fan, H. Liu, Z. Wu, Y. Li, C. Feng, D. Wang, J. Luo, W.M. Wells, S. He
Deep Learning–Based Automatic Segmentation of Lumbosacral Nerves on CT for Spinal Intervention: A Translational Study
American Journal of Neuroradiology Jun 2019, 40 (6) 1074-1081; DOI: 10.3174/ajnr.A6070

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Deep Learning–Based Automatic Segmentation of Lumbosacral Nerves on CT for Spinal Intervention: A Translational Study
G. Fan, H. Liu, Z. Wu, Y. Li, C. Feng, D. Wang, J. Luo, W.M. Wells, S. He
American Journal of Neuroradiology Jun 2019, 40 (6) 1074-1081; DOI: 10.3174/ajnr.A6070
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