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Predicting Stroke Lesion and Clinical Outcome with Random Forests

  • Oskar MaierEmail author
  • Heinz Handels
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10154)

Abstract

The treatment of ischemic stroke requires fast decisions for which the potentially fatal risks of an intervention have to be weighted against the presumed benefits. Ideally, the treating physician could predict the outcome under different circumstances beforehand and thus make an informed treatment decision. To this end, this article presents two new methods: one for lesion outcome and one for clinical outcome prediction from multispectral magnetic resonance sequences. After extracting tailored image features, a random forest classifier respectively regressor is trained. Both approaches were submitted to the Ischemic Stroke Lesion Segmentation (ISLES) 2017 challenge and obtained a first and third place. The outcome underlines the robustness of our designed features and stresses the approach’s resilience against overfitting when faced with small training datasets.

Keywords

Ischemic stroke Lesion segmentation Lesion outcome Clinical outcome mRS Magnetic resonance imaging Brain MR Random forest RDF ISLES 2016 

References

  1. 1.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefzbMATHGoogle Scholar
  2. 2.
    Criminisi, A., Shotton, J.: Decision Forests for Computer Vision and Medical Image Analysis, 1st edn. Springer, London (2013)Google Scholar
  3. 3.
    Forkert, N.D., Siemonsen, S., Dalski, M., et al.: Is there more valuable information in PWI datasets for a voxel-wise acute ischemic stroke tissue outcome prediction than what is represented by typical perfusion maps? In: Molthen, R.C., Weaver, J.B. (eds.) SPIE Medical Imaging, vol. 9038, p. 90381O. International Society for Optics and Photonics (2014)Google Scholar
  4. 4.
    Forkert, N.D., Verleger, T., Cheng, B., et al.: Multiclass support vector machine-based lesion mapping predicts functional outcome in ischemic stroke patients. PLOS ONE 10(6), e0129569 (2015)Google Scholar
  5. 5.
    Galinovic, I.: Evaluation of automated and manual perfusion MRI post-processing: the search for accurate tissue fate prediction in acute ischemic stroke. Ph.D. thesis, Medizinische Fakultät Charité-Universitätsmedizin Berlin (2013)Google Scholar
  6. 6.
    Gonzalez, R.G., Hirsch, J.A., Koroshetz, W.J., Lev, M.H., Schaefer, P.W. (eds.): Acute Ischemic Stroke - Imaging and Intervention, 2 edn. Springer, Berlin (2006)Google Scholar
  7. 7.
    Kemmling, A., Flottmann, F., Forkert, N.D., et al.: Multivariate dynamic prediction of ischemic infarction and tissue salvage as a function of time and degree of recanalization. J. Cereb. Blood Flow Metab. 35(9), 1397–1405 (2015)CrossRefGoogle Scholar
  8. 8.
    Maas, M.B., Lev, M.H., Ay, H., et al.: Collateral vessels on CT angiography predict outcome in acute ischemic stroke. Stroke 40(9), 3001–3005 (2009)CrossRefGoogle Scholar
  9. 9.
    Maier, O.: MedPy - Medical image processing in Python (2016)Google Scholar
  10. 10.
    Maier, O., Handels, H.: MS-lesion segmentation in MRI with random forests. In: Pham, D. (ed.) Longitudinal Multiple Sclerosis Lesion Segmentation Challenge, ISBI (2015)Google Scholar
  11. 11.
    Maier, O., Menze, B.H., von der Gablentz, J., et al.: ISLES 2015 - a public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Med. Image Anal. 35, 250–269 (2017)CrossRefGoogle Scholar
  12. 12.
    Maier, O., Schröder, C., Forkert, N.D., Martinetz, T., Handels, H.: Classifiers for ischemic stroke lesion segmentation: a comparison study. PLOS ONE 10(12), e0145118 (2015)Google Scholar
  13. 13.
    Maier, O., Wilms, M., von der Gablentz, J., et al.: Extra Tree forests for sub-acute ischemic stroke lesion segmentation in MR sequences. J. Neurosci. Methods 240, 89–100 (2015)CrossRefGoogle Scholar
  14. 14.
    Maier, O., Wilms, M., Handels, H.: Image features for brain lesion segmentation using random forests. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds.) BrainLes 2015. LNCS, vol. 9556, pp. 119–130. Springer, Cham (2016). doi: 10.1007/978-3-319-30858-6_11 CrossRefGoogle Scholar
  15. 15.
    Pedregosa, F., Varoquaux, G., Gramfort, A., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Straka, M., Albers, G.W., Bammer, R.: Real-time diffusion-perfusion mismatch analysis in acute stroke. J. Magn. Reson. Imaging 32(5), 1024–37 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Institute of Medical InformaticsUniversität zu LübeckLübeckGermany
  2. 2.Graduate School for Computing in Medicine and Life SciencesUniversität zu LübeckLübeckGermany

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