Abstract
In treating acute ischemic stroke (AIS), determining the time since stroke onset (TSS) is crucial. Computed tomography perfusion (CTP) is vital for determining TSS by providing sufficient cerebral blood flow information. However, the CTP has small samples and high dimensions. In addition, the CTP is multi-map data, which has heterogeneity and complementarity. To address these issues, this paper demonstrates a classification model using CTP to classify the TSS of AIS patients. Firstly, we use dynamic convolution to improve model representation without increasing network complexity. Secondly, we use multi-scale feature fusion to fuse the local correlation of low-order features and use a transformer to fuse the global correlation of higher-order features. Finally, multi-head pooling attention is used to learn the feature information further and obtain as much important information as possible. We use a five-fold cross-validation strategy to verify the effectiveness of our method on the private dataset from a local hospital. The experimental results show that our proposed method achieves at least 5% higher accuracy than other methods in TTS classification task.
P. Yang and Y. Zhang−These authors contributed equally to this work.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Phipps, M.S., Cronin, C.A.: Management of acute ischemic stroke. RMD Open 368, l6983 (2020)
Paciaroni, M., Caso, V., Agnelli, G.: The concept of ischemic penumbra in acute stroke and therapeutic opportunities. Eur. Neurol. 61(6), 321–330 (2009)
Peter-Derex, L., Derex, L.: Wake-up stroke: from pathophysiology to management. Sleep Med. Rev. 48, 101212 (2019)
Konstas, A., Goldmakher, G., Lee, T.-Y., Lev, M.: Theoretic basis and technical implementations of CT perfusion in acute ischemic stroke, part 1: theoretic basis. Am. J. Neuroradiol. 30(4), 662–668 (2009)
Ho, K.C., Speier, W., El-Saden, S., Arnold, C.W.: Classifying acute ischemic stroke onset time using deep imaging features. In: AMIA Annual Symposium Proceedings, pp. 892–901 (2017)
Ho, K.C., Speier, W., Zhang, H., Scalzo, F., El-Saden, S., Arnold, C.W.: A machine learning approach for classifying ischemic stroke onset time from imaging. IEEE Trans. Med. Imaging 38(7), 1666–1676 (2019)
Lee, H., et al.: Machine learning approach to identify stroke within 4.5 hours. Stroke 51(3), 860–866 (2020)
Jiang, L., et al.: Development and external validation of a stability machine learning model to identify wake-up stroke onset time from MRI. Eur. Radiol. 32(6), 3661–3669 (2022)
Zhang, H., et al.: Intra-domain task-adaptive transfer learning to determine acute ischemic stroke onset time. Comput. Med. Imaging Graph. 90, 101926 (2021)
Polson, J.S., et al.: Identifying acute ischemic stroke patients within the thrombolytic treatment window using deep learning. J. Neuroimaging 32(6), 1153–1160 (2022)
Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., Liu, Z.: Dynamic convolution: attention over convolution kernels. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11030–11039 (2020)
Rosenblatt, F.: The perceptron, a perceiving and recognizing automaton Project Para. Cornell Aeronautical Laboratory (1957)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems. Vol. 30 (2017)
Dosovitskiy, A., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
Qi, D., Su, L., Song, J., Cui, E., Bharti, T., Sacheti, A.: ImageBERT: cross-modal pre-training with large-scale weak-supervised image-text data. arXiv preprint arXiv:2001.07966 (2020)
Chen, M., Radford, A., Child, R., Wu, J., Jun, H., Luan, D., Sutskever, I.: Generative pretraining from pixels. In: International Conference on Machine Learning, pp. 1691–1703. PMLR (2020)
Sun, C., Myers, A., Vondrick, C., Murphy, K., Schmid, C.: VideoBERT: a joint model for video and language representation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7464–7473 (2019)
Chitta, K., Prakash, A., Jaeger, B., Yu, Z., Renz, K., Geiger, A.: TransFuser: Imitation with transformer-based sensor fusion for autonomous driving. IEEE Trans. Pattern Anal. Mach. Intell. (2022). https://doi.org/10.1109/TPAMI.2022.3200245
Dai, Z., Liu, H., Le, Q.V., Tan, M.: CoatNet: marrying convolution and attention for all data sizes. Adv. Neural. Inf. Process. Syst. 34, 3965–3977 (2021)
Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4489–4497 (2015)
Carreira, J., Zisserman, A.: Quo vadis, action recognition? A new model and the kinetics dataset. In: proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017)
Chen, Y., Kalantidis, Y., Li, J., Yan, S., Feng, J.: Multi-fiber networks for video recognition. In: Proceedings of the European Conference on Computer Vision, pp. 352–367 (2018)
Sun, L., Zhao, G., Zheng, Y., Wu, Z.: Spectral–spatial feature tokenization transformer for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 60, 1–14 (2022)
Feichtenhofer, C., Fan, H., Malik, J., He, K.: SlowFast networks for video recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6202–6211 (2019)
Zhu, H., Jiang, L., Zhang, H., Luo, L., Chen, Y., Chen, Y.: An automatic machine learning approach for ischemic stroke onset time identification based on DWI and FLAIR imaging. NeuroImage: Clin. 31, 102744 (2021)
Polson, J.S., et al.: Deep learning approaches to identify patients within the thrombolytic treatment window (2022).https://doi.org/10.1111/jon.13043
Zhang, Y.-Q., et al.: MRI radiomic features-based machine learning approach to classify ischemic stroke onset time. J. Neurol. 269(1), 350–360 (2022)
Acknowledgement
This work was supported National Natural Science Foundation of China (Nos. 62201360, 62101338, 61871274, and U1902209), National Natural Science Foundation of Guangdong Province (2019A1515111205), Guangdong Basic and Applied Basic Research (2021A1515110746), Shenzhen Key Basic Research Project (KCXFZ20201221173213036, JCYJ20220818095809021, SGDX202011030958020–07, JCYJ201908081556188–06, and JCYJ20190808145011259) Capital’s Funds for Health Improvement and Research (No. 2022–1-2031), Beijing Hospitals Authority’s Ascent Plan (No. DFL20220303), and Beijing Key Specialists in Major Epidemic Prevention and Control.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yang, P., Zhang, Y., Lei, H., Bian, Y., Yang, Q., Lei, B. (2023). Acute Ischemic Stroke Onset Time Classification with Dynamic Convolution and Perfusion Maps Fusion. 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_54
Download citation
DOI: https://doi.org/10.1007/978-3-031-43904-9_54
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)
