Abstract
While the number of stroke patients is increasing worldwide and every fifth stroke survivor is developing long-term cognitive impairment, its prediction becomes more and more important. In this work, we address the challenge of predicting any long-term cognitive impairment after a stroke using deep learning. We explore multi-task learning that combines the cognitive classification with the segmentation of brain lesions such as infarct and white matter hyperintensities or the reconstruction of the brain. Our approach is further expanded to include clinical non-imaging data to the input imaging information. The multi-task model using an autoencoder for reconstruction achieved the highest performance in classifying post-stroke cognitive impairment when only imaging data is used. The performance can be further improved by incorporating clinical information using a previously proposed dynamic affine feature map transformation. We developed and tested our approach on an in-house acquired dataset of magnetic resonance images specifically used to visualize stroke damage right after stroke occurrence. The patients were followed-up after one year to assess their cognitive status. The multi-task model trained on infarct segmentation on diffusion tensor images and enriched with clinical non-imaging information achieved the best overall performance with a balanced accuracy score of 70.3% and an area-under-the-curve of 0.791.
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
Betrouni, N., Yasmina, M., Bombois, S., Pétrault, M., Dondaine, T., Lachaud, C.: Texture features of magnetic resonance images: an early marker of post-stroke cognitive impairment. Transl. Stroke Res. 11(4), 643–652 (2020). https://doi.org/10.1007/s12975-019-00746-3
Longstreth, W.T., Diehr, P.H., Yee, L.M., Newman, A.B., Beauchamp, N.J.: Brain imaging findings in elderly adults and years of life, healthy life, and able life over the ensuing 16 years: the Cardiovascular health study. J. Am. Geriatr. Soc. 62(10), 1838–1843 (2014)
Zietemann, V., et al.: Early MoCA predicts long-term cognitive and functional outcome and mortality after stroke. Neurology 91(20), e1838–e1850 (2018)
Georgakis, M.K., et al.: Cerebral small vessel disease burden and cognitive and functional outcomes after stroke: a multicenter prospective cohort study. Alzheimer’s Dementia (2022)
Hénon, H., Pasquier, F., Leys, D.: Poststroke dementia. Cerebrovasc. Dis. 22(1), 61–70 (2006)
Weaver, N.A., Kuijf, H.J., Aben, H.P., Abrigo, J., Bae, H.-J., Barbay, M., et al.: Strategic infarct locations for post-stroke cognitive impairment: a pooled analysis of individual patient data from 12 acute Ischaemic stroke cohorts. Lancet Neurol. 20(6), 448–459 (2021)
Zhang, Y., Yang, Q.: A survey on multi-task learning. arXiv preprint arXiv:1707.08114 (2017)
Zimmer, V.A., et al.: Placenta segmentation in ultrasound imaging: addressing sources of uncertainty and limited field-of-view. arXiv preprint arXiv:2206.14746 (2022)
Crawshaw, M.: Multi-task learning with deep neural networks: a survey. arXiv preprint arXiv:2009.09796 (2020)
Zhou, Y., et al.: Multi-task learning for segmentation and classification of tumors in 3D automated breast ultrasound images. Med. Imag. Anal. 70, 101918 (2021)
Lopes, R., et al.: Prediction of long-term cognitive function after minor stroke using functional connectivity. Neurology 96(8), e1167–e1179 (2021)
Jetley, S., Lord, N.A., Lee, N., Torr, P.H.S.: Learn To Pay Attention. Proc, ICLR (2018)
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
Bank, D., Koenigstein, N., Giryes, R.: Autoencoders. arXiv preprint arXiv:2003.05991 (2020)
Pölsterl, S., Wolf, T.N., Wachinger, C.: Combining 3D image and tabular data via the dynamic affine feature map transform. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12905, pp. 688–698. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87240-3_66
Pérez-García, F., Sparks, R., Ourselin, S.: TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning. Comput. Meth. Programs Biomed. 208, 106236 (2021)
Ledig, C., et al.: Robust whole-brain segmentation: application to traumatic brain injury. Med. Image Anal. 21(1), 40–58 (2015)
Evans, A.C., Collins, D.L., Mills, S.R., Brown, E.D., Kelly, R.L., Peters, T.M.: 3D statistical neuroanatomical models from 305 MRI volumes. In: Proceeding IEEE- Nuclear Science Symposium and Medical Imaging Conference, pp. 1813–1827 (1993)
Avants, B.B., Tustison, N., Song, G.: Advanced normalization tools (ANTS). Insight J. 2(365), 1–35 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Binzer, M., Hammernik, K., Rueckert, D., Zimmer, V.A. (2022). Long-Term Cognitive Outcome Prediction in Stroke Patients Using Multi-task Learning on Imaging and Tabular Data. In: Rekik, I., Adeli, E., Park, S.H., Cintas, C. (eds) Predictive Intelligence in Medicine. PRIME 2022. Lecture Notes in Computer Science, vol 13564. Springer, Cham. https://doi.org/10.1007/978-3-031-16919-9_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-16919-9_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16918-2
Online ISBN: 978-3-031-16919-9
eBook Packages: Computer ScienceComputer Science (R0)
