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Research ArticleAdult Brain
Open Access

Unsupervised Deep Learning for Stroke Lesion Segmentation on Follow-up CT Based on Generative Adversarial Networks

H. van Voorst, P.R. Konduri, L.M. van Poppel, W. van der Steen, P.M. van der Sluijs, E.M.H. Slot, B.J. Emmer, W.H. van Zwam, Y.B.W.E.M. Roos, C.B.L.M. Majoie, G. Zaharchuk, M.W.A. Caan and H.A. Marquering on behalf of the CONTRAST Consortium Collaborators
American Journal of Neuroradiology July 2022, DOI: https://doi.org/10.3174/ajnr.A7582
H. van Voorst
aFrom the Departments of Radiology and Nuclear Medicine (H.v.V., P.R.K., L.M.v.P., B.J.E., C.B.L.M.M., H.A.M.)
bBiomedical Engineering and Physics (H.v.V., P.R.K., L.M.v.P., M.W.A.C., H.A.M.)
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P.R. Konduri
aFrom the Departments of Radiology and Nuclear Medicine (H.v.V., P.R.K., L.M.v.P., B.J.E., C.B.L.M.M., H.A.M.)
bBiomedical Engineering and Physics (H.v.V., P.R.K., L.M.v.P., M.W.A.C., H.A.M.)
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L.M. van Poppel
aFrom the Departments of Radiology and Nuclear Medicine (H.v.V., P.R.K., L.M.v.P., B.J.E., C.B.L.M.M., H.A.M.)
bBiomedical Engineering and Physics (H.v.V., P.R.K., L.M.v.P., M.W.A.C., H.A.M.)
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W. van der Steen
dDepartments of Neurology (W.v.d.S., P.M.v.d.S.)
eRadiology and Nuclear Medicine (W.v.d.S., P.M.v.d.S.), Erasmus University Medical Center, Rotterdam, the Netherlands
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P.M. van der Sluijs
dDepartments of Neurology (W.v.d.S., P.M.v.d.S.)
eRadiology and Nuclear Medicine (W.v.d.S., P.M.v.d.S.), Erasmus University Medical Center, Rotterdam, the Netherlands
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E.M.H. Slot
fDepartment of Neurology and Neurosurgery (E.M.H.S.), University Medical Center Utrecht, Utrecht, the Netherlands
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B.J. Emmer
aFrom the Departments of Radiology and Nuclear Medicine (H.v.V., P.R.K., L.M.v.P., B.J.E., C.B.L.M.M., H.A.M.)
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W.H. van Zwam
gDepartment of Radiology and Nuclear Medicine (W.H.v.Z.), Maastricht University Medical Center, Maastricht, the Netherlands
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Y.B.W.E.M. Roos
cNeurology (Y.B.W.E.M.R.), Faculty of Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
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C.B.L.M. Majoie
aFrom the Departments of Radiology and Nuclear Medicine (H.v.V., P.R.K., L.M.v.P., B.J.E., C.B.L.M.M., H.A.M.)
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G. Zaharchuk
hDepartment of Radiology (G.Z.), Stanford University, Stanford, California
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M.W.A. Caan
bBiomedical Engineering and Physics (H.v.V., P.R.K., L.M.v.P., M.W.A.C., H.A.M.)
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H.A. Marquering
aFrom the Departments of Radiology and Nuclear Medicine (H.v.V., P.R.K., L.M.v.P., B.J.E., C.B.L.M.M., H.A.M.)
bBiomedical Engineering and Physics (H.v.V., P.R.K., L.M.v.P., M.W.A.C., H.A.M.)
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  • FIG 1.
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    FIG 1.

    The FU2BL-GAN global architecture (asterisk). The follow-up (FU) NCCT with lesion is clipped between Hounsfield unit ranges of 0−100 and 100−1000 and normalized to (−1) (double asterisks). The original BL NCCT is only clipped between 0 and 100 HU and normalized to (−1). The FU NCCT with a lesion is passed through the generator network to compute a difference map. This difference map is subtracted from the input FU NCCT to construct a generated BL NCCT. Original BL and generated BL are optimized on the basis of the absolute voxelwise difference (L1-loss) and the binary cross-entropy loss (adversarial-loss) of the discriminator networks classification (original or generated BL).

  • FIG 2.
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    FIG 2.

    Patients included in the training, validation, and test sets. The training data consisted of a BL and at least 1 follow-up (FU) NCCT. FU of <8 hours: FU NCCT acquired within 8 hours; FU 24H: FU NCCT acquired 8–72 hours; FU 1W: FU NCCT acquired 72  hours to 2 weeks after endovascular treatment or randomization. Validation and test sets were constructed with data from the studies by Konduri et al18 and Hssayeni et al.19 8H indicates 8 hours.

  • FIG 3.
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    FIG 3.

    Dice similarity coefficients of test sets: 24-hour follow-up after AIS (24H infarct), 1-week follow-up after AIS (1W infarct), HT, and PrH. A, The results of all the test set data. B, Only results from lesions that are >10 mL. Each shade of color represents the results based on the supervised nnUnet approach, the FU2BL-GAN approach trained with L1+adv, and the generator trained with L1-loss only (L1) respectively. The Asterisk indicates P < .05; double asterisks, P < .001; triple asterisks, P < 1e-10; NS, nonsignificant difference.

  • FIG 4.
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    FIG 4.

    Bland-Altman plots of predicted lesion size for the FU2BL-GAN. A, 24H infarct follow-up. B, 1W infarct follow-up. C, HT. D, PrH.

  • FIG 5.
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    FIG 5.

    Visual results of the FU2BL-GAN. The first column contains the input NCCT with lesion used as input for the generator model to generate a difference map (column 2). The difference map is subtracted from the input NCCT (column 1) to obtain a generated BL scan (column 3). The negative (blue) and positive (red) values of the difference map correspond to the deviation of the difference map from zero. A higher deviation from zero implies a higher attenuation adjustment of the follow-up NCCT to generate the BL NCCT without a lesion. Column 4 contains the ground truth lesion annotations. Arrows show false-positive hemorrhage (rows 3 and 4), false-negative infarct (row 5, upper arrow), false-positive infarct (row 5, lower arrow), and false-negative hemorrhage segmentation (arrow, row 6).

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H. van Voorst, P.R. Konduri, L.M. van Poppel, W. van der Steen, P.M. van der Sluijs, E.M.H. Slot, B.J. Emmer, W.H. van Zwam, Y.B.W.E.M. Roos, C.B.L.M. Majoie, G. Zaharchuk, M.W.A. Caan, H.A. Marquering
Unsupervised Deep Learning for Stroke Lesion Segmentation on Follow-up CT Based on Generative Adversarial Networks
American Journal of Neuroradiology Jul 2022, DOI: 10.3174/ajnr.A7582

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Unsupervised Deep Learning for Stroke Lesion Segmentation on Follow-up CT Based on Generative Adversarial Networks
H. van Voorst, P.R. Konduri, L.M. van Poppel, W. van der Steen, P.M. van der Sluijs, E.M.H. Slot, B.J. Emmer, W.H. van Zwam, Y.B.W.E.M. Roos, C.B.L.M. Majoie, G. Zaharchuk, M.W.A. Caan, H.A. Marquering
American Journal of Neuroradiology Jul 2022, DOI: 10.3174/ajnr.A7582
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