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Research ArticleAdult Brain
Open Access

Automated Segmentation of Intracranial Thrombus on NCCT and CTA in Patients with Acute Ischemic Stroke Using a Coarse-to-Fine Deep Learning Model

K. Zhu, F. Bala, J. Zhang, F. Benali, P. Cimflova, B.J. Kim, R. McDonough, N. Singh, M.D. Hill, M. Goyal, A. Demchuk, B.K. Menon and W. Qiu
American Journal of Neuroradiology May 2023, DOI: https://doi.org/10.3174/ajnr.A7878
K. Zhu
aFrom the Department of Clinical Neurosciences and Hotchkiss Brain Institute (K.Z., F. Bala, J.Z., F. Benali, P.C., R.M., N.S., M.D.H., M.G., A.D., B.K.M.)
dCollege of Electronic Engineering (K.Z.), Xi’an Shiyou University, Xi’an, Shaanxi, China
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F. Bala
aFrom the Department of Clinical Neurosciences and Hotchkiss Brain Institute (K.Z., F. Bala, J.Z., F. Benali, P.C., R.M., N.S., M.D.H., M.G., A.D., B.K.M.)
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J. Zhang
aFrom the Department of Clinical Neurosciences and Hotchkiss Brain Institute (K.Z., F. Bala, J.Z., F. Benali, P.C., R.M., N.S., M.D.H., M.G., A.D., B.K.M.)
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F. Benali
aFrom the Department of Clinical Neurosciences and Hotchkiss Brain Institute (K.Z., F. Bala, J.Z., F. Benali, P.C., R.M., N.S., M.D.H., M.G., A.D., B.K.M.)
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P. Cimflova
aFrom the Department of Clinical Neurosciences and Hotchkiss Brain Institute (K.Z., F. Bala, J.Z., F. Benali, P.C., R.M., N.S., M.D.H., M.G., A.D., B.K.M.)
cDepartment of Medicine, and Department of Radiology (P.C., M.D.H., A.D.), Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
eSt. Anne’s University Hospital Brno and Faculty of Medicine (P.C.), Masaryk University, Brno, Czech Republic
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B.J. Kim
fDepartment of Neurology and Cerebrovascular Center (B.J.K.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea
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R. McDonough
aFrom the Department of Clinical Neurosciences and Hotchkiss Brain Institute (K.Z., F. Bala, J.Z., F. Benali, P.C., R.M., N.S., M.D.H., M.G., A.D., B.K.M.)
gDepartment of Diagnostic and Interventional Neuroradiology (R.M.), University Hospital Hamburg, Hamburg, Germany
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N. Singh
aFrom the Department of Clinical Neurosciences and Hotchkiss Brain Institute (K.Z., F. Bala, J.Z., F. Benali, P.C., R.M., N.S., M.D.H., M.G., A.D., B.K.M.)
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M.D. Hill
aFrom the Department of Clinical Neurosciences and Hotchkiss Brain Institute (K.Z., F. Bala, J.Z., F. Benali, P.C., R.M., N.S., M.D.H., M.G., A.D., B.K.M.)
bDepartment of Community Health Sciences (M.D.H.)
cDepartment of Medicine, and Department of Radiology (P.C., M.D.H., A.D.), Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
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M. Goyal
aFrom the Department of Clinical Neurosciences and Hotchkiss Brain Institute (K.Z., F. Bala, J.Z., F. Benali, P.C., R.M., N.S., M.D.H., M.G., A.D., B.K.M.)
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A. Demchuk
aFrom the Department of Clinical Neurosciences and Hotchkiss Brain Institute (K.Z., F. Bala, J.Z., F. Benali, P.C., R.M., N.S., M.D.H., M.G., A.D., B.K.M.)
cDepartment of Medicine, and Department of Radiology (P.C., M.D.H., A.D.), Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
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B.K. Menon
aFrom the Department of Clinical Neurosciences and Hotchkiss Brain Institute (K.Z., F. Bala, J.Z., F. Benali, P.C., R.M., N.S., M.D.H., M.G., A.D., B.K.M.)
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W. Qiu
hSchool of Life Science and Technology (W.Q.), Huazhong University of Science and Technology, Wuhan, Hubei, China
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    FIG 1.

    Flowchart of patient inclusion.

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    FIG 2.

    The proposed coarse-to-fine DL architecture. A 2-stage coarse-to-fine thrombus segmentation neural network used a multiscale training strategy with a deep-supervision mechanism, which consisted of the channel and spatial attention blocks and the scale-aware pyramid fusion module.

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    FIG 3.

    Two segmentation examples obtained by the proposed model. The images are shown as NCCT/CTA overlaid with the manually or algorithm-segmented thrombi in red.

Tables

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    Table 1:

    Patient characteristics in the derivation data and in the internal and external test dataa

    CharacteristicDerivation Set (n = 329)Internal Test Set (n = 170)External Test Set (n = 83)
    Age (yr)b69 (59–78)68 (59–79)71 (63–79)
    Male173 (52.6)89 (52.4)43 (51.8)
    Race
     White261 (79.3)142 (83.5)NA
     Asian26 (7.9)10 (5.9)
     African American35 (10.6)14 (8.2)
     Other7 (2.1)4 (2.4)
    Onset-to-CT timeb160 (82–268)154 (79–284)120 (89–184)
    Baseline NIHSS score b17 (12–21)16 (13–20)9 (5–15)
    ASPECTSb8 (7–9)8 (7–9)10 (8–10)
    Hypertension222 (67.5)127 (74.7)49 (59.0)
    Hyperlipidemia140 (42.6)79 (46.5)NA
    Diabetes70 (21.3)36 (21.2)9 (10.8)
    IV alteplase193 (58.7)99 (58.2)66 (79.5)
    IV nerinetide155 (47.1)87 (51.2)0
    Occlusion site
     ICA71 (21.6)39 (23.0)8 (9.6)
     M1, MCA248 (75.4)125 (73.5)26 (31.3)
     M2, MCA10 (3.0)6 (3.5)10 (12.1)
     M3/M4, MCA0013 (15.7)
     ACA (A2/A3)003 (3.6)
     PCA (P2)002 (2.4)
     No occlusion0021 (25.3)
    • Note:—ACA indicates anterior cerebral artery; PCA, posterior cerebral artery; NA, not applicable.

    • ↵a Except where indicated, data are number of patients, with percentages in parentheses.

    • ↵b Data are the median with the IQR in parentheses.

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    Table 2:

    Quantitative evaluation of thrombus measurement in the internal test data (170 patients) and external validation data (34 patients with LVO) of PRoVe-ITa

    Internal Validation Data (n = 170)External Validation Data (n = 34)
    DC (%)70.7 (58.0–77.8)66.8 (58.5–74.6)
    ASSD (mm)0.38 (0.24 to 0.77)0.56 (0.19−1.21)
    HD95 (mm)1.31 (0.79−3.83)3.05 (1.37−8.19)
    Reference length (mm)20.3 (10.25–34.73)12.11 (5.73–19.51)
    Prediction length (mm)13.94 (6.32−25.76)9.0 (3.4–21.74)
    Embedded Image−5.41 (−11.34 to −0.5)0.0 (−6.12−8.2)
    Embedded Image 5.79 (1.79−11.37)6.14 (2.81−12.27)
    Reference volume (mm³)80.53 (49.92–155.39)37.51 (29.19–78.3)
    Prediction volume (mm³)71.9 (39.25–126.15)58.14 (32.96–104.68)
    Embedded Image (mm³)−0.9 (−3.41−0.33)0.59 (−0.17−4.53)
    Embedded Image (mm³)1.76 (0.71−4.23)1.47 (0.5−4.62)
    Volume correlation0.95 (0.89–0.98)0.91 (0.84–0.96)
    • ↵a Values are shown as medians with the IQR in parentheses. Embedded Image and Embedded Imagerepresent relative and absolute, error, respectively.

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K. Zhu, F. Bala, J. Zhang, F. Benali, P. Cimflova, B.J. Kim, R. McDonough, N. Singh, M.D. Hill, M. Goyal, A. Demchuk, B.K. Menon, W. Qiu
Automated Segmentation of Intracranial Thrombus on NCCT and CTA in Patients with Acute Ischemic Stroke Using a Coarse-to-Fine Deep Learning Model
American Journal of Neuroradiology May 2023, DOI: 10.3174/ajnr.A7878

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Automated Segmentation of Intracranial Thrombus on NCCT and CTA in Patients with Acute Ischemic Stroke Using a Coarse-to-Fine Deep Learning Model
K. Zhu, F. Bala, J. Zhang, F. Benali, P. Cimflova, B.J. Kim, R. McDonough, N. Singh, M.D. Hill, M. Goyal, A. Demchuk, B.K. Menon, W. Qiu
American Journal of Neuroradiology May 2023, DOI: 10.3174/ajnr.A7878
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