Abstract
While acute ischemic stroke due to large vessel occlusion (LVO) may be life-threatening or permanently disabling, timely intervention with endovascular thrombectomy (EVT) can prove life-saving for affected patients. Appropriate patient selection based on prognostic prediction is vital for this costly and invasive procedure, as not all patients will benefit from EVT. Accurate prognostic prediction for LVO presents a significant challenge. Computed Tomography Perfusion (CTP) maps can provide additional information for clinicians to make decisions. However, CTP maps are not always available due to variations in available equipment, funding, expertise and image quality. To address these gaps, we test (i) the utility of acquired CTP maps in a deep learning prediction model, (ii) the ability to improve flexibility of this model through image synthesis, and (iii) the added benefits of including multi-task learning with a simple clinical task to focus the synthesis on key clinical features. Our results demonstrate that network architectures utilising a full set of images can still be flexibly deployed if CTP maps are unavailable as their benefits can be effectively synthesized from more widely available images (NCCT and CTA). Additionally, such synthesized images may help with interpretability and building a clinically trusted model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Malhotra, K., Gornbein, J., Saver, J.L.: Ischemic strokes due to large-vessel occlusions contribute disproportionately to stroke-related dependence and death: a review. Front. Neurol. 8, 651 (2017)
Powers, W.J., et al.: 2018 guidelines for the early management of patients with acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 49(3), e46–e99 (2018)
Wolman, D.N., et al.: Endovascular versus medical therapy for large-vessel anterior occlusive stroke presenting with mild symptoms. Int. J. Stroke 15(3), 324–331 (2020)
Bacchi, S., Zerner, T., Oakden-Rayner, L., Kleinig, T., Patel, S., Jannes, J.: Deep learning in the prediction of ischaemic stroke thrombolysis functional outcomes: a pilot study. Acad. Radiol. 27(2), e19–e23 (2020)
Samak, Z.A., Clatworthy, P., Mirmehdi, M.: Transop: transformer-based multimodal classification for stroke treatment outcome prediction. arXiv preprint arXiv:2301.10829 (2023)
Zeng, M., et al.: Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: a systematic review and meta-analysis. Front. Neurol. 13, 945813 (2022)
Saleem, Y., et al.: Acute neurological deterioration in large vessel occlusions and mild symptoms managed medically. Stroke 51(5), 1428–1434 (2020)
Nogueira, R.G., et al.: Thrombectomy 6 to 24 hours after stroke with a mismatch between deficit and infarct. New Engl. J. Med. 378(1), 11–21 (2018)
Bal, S., et al.: Time dependence of reliability of noncontrast computed tomography in comparison to computed tomography angiography source image in acute ischemic stroke. Int. J. Stroke 10(1), 55–60 (2015)
Chu, Y., et al.: Comparison of time consumption and success rate between CT angiography-and CT perfusion-based imaging assessment strategy for the patients with acute ischemic stroke. BMC Med. Imaging 22(1), 1–8 (2022)
Chung, C.Y., Hu, R., Peterson, R.B., Allen, J.W.: Automated processing of head CT perfusion imaging for ischemic stroke triage: a practical guide to quality assurance and interpretation. Am. J. Roentgenol. 217(6), 1401–1416 (2021)
Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020)
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning, pp. 214–223. PMLR (2017)
Zhu, J.-Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
Jenkinson, M., Smith, S.: A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5(2), 143–156 (2001)
Muschelli, J.: A publicly available, high resolution, unbiased CT brain template. In: Lesot, M.-J., et al. (eds.) IPMU 2020. CCIS, vol. 1239, pp. 358–366. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50153-2_27
Waaijer, A., et al.: Reproducibility of quantitative CT brain perfusion measurements in patients with symptomatic unilateral carotid artery stenosis. Am. J. Neuroradiol. 28(5), 927–932 (2007)
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zeng, M. et al. (2023). Improved Flexibility and Interpretability of Large Vessel Stroke Prognostication Using Image Synthesis and Multi-task Learning. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14224. Springer, Cham. https://doi.org/10.1007/978-3-031-43904-9_67
Download citation
DOI: https://doi.org/10.1007/978-3-031-43904-9_67
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43903-2
Online ISBN: 978-3-031-43904-9
eBook Packages: Computer ScienceComputer Science (R0)