Prediction of Ischemic Stroke Lesion and Clinical Outcome in Multi-modal MRI Images Using Random Forests

  • Qaiser MahmoodEmail author
  • A. Basit
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10154)


Herein, we present an automated segmentation method for ischemic stroke lesion segmentation in multi-modal MRI images. The method is based on an ensemble learning technique called random forest (RF), which generates several classifiers and combines their results in order to make decisions. In RF, we employ several meaningful features such as intensities, entropy, gradient etc. to classify the voxels in multi-modal MRI images. The segmentation method is validated on both training and testing data, obtained from MICCAI ISLES-2016 challenge dataset. The evaluation of the method is done by performing two tasks: ischemic stroke lesion outcome prediction (Task I) and clinical outcome prediction (Task II). For Task I, the performance of the method is evaluated relative to the manual segmentation, done by the clinical experts. For Task II, the performance of the method is evaluated relative to the 90 days mRS score, provided as ground truths by ISLES-2016 challenge organizers. The experimental results show the robustness of the segmentation method, and that it achieves reasonable accuracy for the prediction of both ischemic stroke lesion and clinical outcome in multi-modal MRI images.


Segmentation Automatic MRI Ischemic stroke lesion Random forests ISLES-2016 


  1. 1.
  2. 2.
    Fassbender, K., Balucani, C., Walter, S., Levine, S.R., Haass, A., Grotta, J.: Streamlining of prehospital stroke management: the golden hour. Lancet Neurol. 12, 585–596 (2013)CrossRefGoogle Scholar
  3. 3.
    Feigin, V.L., Lawes, C.M., Bennett, D.A., Barker-Collo, S.L., Parag, V.: Worldwide Stroke incidence and early case fatality reported in 56 population-based studies: a systematic review. Lancet Neurol. 8, 355–369 (2009)CrossRefGoogle Scholar
  4. 4.
    Qaiser, M., Shaochuan, L., Andreas, F., Stefan, C., Artur, C., Andrew, M., Mikael, P.: A comparative study of automated segmentation methods for use in a microwave tomography system for imaging intracerebral hemorrhage in stroke patients. J. Electromagn. Anal. Appl. (JEMAA) 7, 152–167 (2015)Google Scholar
  5. 5.
    Ball, J.B., Pensak, M.L.: Fundamentals of magnetic resonance imaging. Am. J. Otol. 8, 81–85 (1987)Google Scholar
  6. 6.
    Moumen, T., E., Hashim, M., M.: Tumor segmentation in brain MRI using a fuzzy approach with class center priors. EURASIP J. Image Video Process., online (2014)Google Scholar
  7. 7.
    Oskar, M., Matthias, W., von der Janina, G., Ulrike, M.K., Thomas, F.M., Heinz, H.: Extra Tree forests for sub-acute ischemic stroke lesion segmentation in MR sequences. J. Neurosci. Methods 240, 89–100 (2014)Google Scholar
  8. 8.
    Rekik, I., Allassonniere, S., Carpenter, T.K., Wardlaw, J.M.: Medical image analysis methods In MR/CT-imaged acute-subacute ischemic stroke lesion: segmentation, prediction and insights into dynamic evolution simulation models. Critical Appraisal. NeuroImage Clinical 1, 164–178 (2012)CrossRefGoogle Scholar
  9. 9.
    Mitra, J., Bourgeat, P., Fripp, J., Ghose, S., et al.: Lesion segmentation from multimodal MRI using random forests following ischemic stroke. NeuroImage 98, 324–335 (2014)CrossRefGoogle Scholar
  10. 10.
    Seghier, M.L., Ramlackhansingh, A., Crinion, J., Leff, A.P., Price, C.J.: Lesion identification using unified segmentation-normalisation models and fuzzy clustering. NeuroImage 41, 1253–1266 (2008)CrossRefGoogle Scholar
  11. 11.
    Forbes, F., Doyle, S., Garcia-Lorenzo, D., Barillot, C., Dojat, M.: Adaptive weighted fusion of multiple MR sequences for brain lesion segmentation. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), pp. 69–72 (2010)Google Scholar
  12. 12.
    Oskar, M., Björn, M., Matthias, L., Stefan, W., 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
  13. 13.
    Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)CrossRefzbMATHGoogle Scholar
  14. 14.
    Criminisi, A., Shotton, J.: Decision forests for Computer Vision and Medical Image Analysis. Advances in Computer Vision and Pattern Recognition. Springer, London (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Pakistan Institute of Nuclear Science and TechnologyIslamabadPakistan

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