Abstract
Cerebral infarct volume measured in follow-up non-contrast CT (NCCT) scans is an important radiologic outcome measure evaluating the effectiveness of endovascular therapy of acute ischemic stroke (AIS) patients. In this paper, a dense Multi-Path Contextual Generative Adversarial Network (MPC-GAN) is proposed to automatically segment ischemic infarct volume from NCCT images of AIS patients. The developed MPC-GAN approach makes use of a dense multi-path U-Net as generator regularized by a discriminator network. Both generator and discriminator take contextual information as inputs, such as bilateral intensity difference, infarct location probability, and distance to cerebrospinal fluid (CSF). We collected 100 NCCT images with manual segmentations. Of 100 patients, 60 patients were used to train the MPC-GAN, 10 patients were used to tune the parameters, and the remained 30 patients were used for validation. Quantitative results in comparison with manual segmentations show that the proposed MPC-GAN achieved a dice coefficient (DC) of 72.6%, outperforming some state-of-the-art segmentation methods, such as U-Net, U-Net based GAN, and random forest based segmentation method.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Goyal, M., et al.: Randomized assessment of rapid endovascular treatment of ischemic stroke. N. Engl. J. Med. 372(11), 1019–1030 (2015)
Boers, A.M., et al.: Mediation of the relationship between endovascular therapy and functional outcome by follow-up infarct volume in patients with acute ischemic stroke. JAMA Neurol. 76, 194–202 (2019)
Winzeck, S., et al.: ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI. Front. Neurol. 9, 679 (2018)
Gillebert, C.R., Humphreys, G.W., Mantini, D.: Automated delineation of stroke lesions using brain CT images. NeuroImage Clin. 4, 540–548 (2014)
de Haan, B., Clas, P., Juenger, H., Wilke, M., Karnath, H.O.: Fast semi-automated lesion demarcation in stroke. NeuroImage Clin. 9, 69–74 (2015)
Kuang, H., Menon, B.K., Qiu, W.: Segmenting hemorrhagic and ischemic infarct simultaneously from follow-up non-contrast CT images in patients with acute ischemic stroke. IEEE Access 7, 39842–39851 (2019)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Kervadec, H., Bouchtiba, J., Desrosiers, C., Granger, É., Dolz, J., Ayed, I.B.: Boundary loss for highly unbalanced segmentation. arXiv preprint arXiv:1812.07032 (2018)
Qiu, W., Yuan, J., Ukwatta, E., Sun, Y., Rajchl, M., Fenster, A.: Prostate segmentation: an efficient convex optimization approach with axial symmetry using 3D TRUS and MR images. IEEE Trans. Med. Imaging 33(4), 1–14 (2014)
Qiu, W., et al.: Automatic segmentation approach to extracting neonatal cerebral ventricles from 3D ultrasound images. Med. Image Anal. 35, 181–191 (2017)
Kuang, H., Najm, M., Menon, B.K., Qiu, W.: Joint segmentation of intracerebral hemorrhage and infarct from non-contrast CT images of post-treatment acute ischemic stroke patients. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 681–688. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00931-1_78
Boers, A.M., et al.: Automated cerebral infarct volume measurement in follow-up noncontrast ct scans of patients with acute ischemic stroke. Am. J. Neuroradiol. 34(8), 1522–1527 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Kuang, H., Menon, B.K., Qiu, W. (2019). Automated Infarct Segmentation from Follow-up Non-Contrast CT Scans in Patients with Acute Ischemic Stroke Using Dense Multi-Path Contextual Generative Adversarial Network. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11766. Springer, Cham. https://doi.org/10.1007/978-3-030-32248-9_95
Download citation
DOI: https://doi.org/10.1007/978-3-030-32248-9_95
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32247-2
Online ISBN: 978-3-030-32248-9
eBook Packages: Computer ScienceComputer Science (R0)
