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Image Features for Brain Lesion Segmentation Using Random Forests

  • Conference paper
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9556))

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Abstract

From clinical practice as well as research methods arises the need for accurate, reproducible and reliable segmentation of pathological areas from brain MR scans. This paper describes a set of hand-selected, voxel-based image features highly suitable for the tissue discrimination task. Embedded in a random decision forest framework, the proposed method was applied to sub-acute ischemic stroke (ISLES 2015 - SISS), acute ischemic stroke (ISLES 2015 - SPES) and glioma (BRATS 2015) segmentation with only minor adaptation. For all of these three challenges, our generic approach received high ranks, among them a second place. The outcome underlines the robustness of our features for segmentation in brain MR, while simultaneously stressing the necessity for highly specialized solution to achieve state-of-the-art performance.

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Notes

  1. 1.

    http://isles-challenge.org.

  2. 2.

    http://braintumorsegmentation.org.

References

  1. Astrup, J., Siesjö, B.K., Symon, L.: Thresholds in cerebral ischemia-the ischemic penumbra. Stroke 12(6), 723–725 (1981)

    Article  Google Scholar 

  2. Cireşan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, Nassir (eds.) MICCAI 2013, Part II. LNCS, vol. 8150, pp. 411–418. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  3. Criminisi, A., Shotton, J., Konukoglu, E.: Decision forests: a unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Found. Trends\({\textregistered }\) Comput. Graph. 7(2–3), 81–227 (2012)

    Google Scholar 

  4. Filippi, M., Rocca, M.A.: MR imaging of multiple sclerosis. Radiology 259(3), 659–681 (2011). http://www.ncbi.nlm.nih.gov/pubmed/21602503

    Article  Google Scholar 

  5. Ghosh, N., Yuan, X., Turenius, C.I., et al.: Automated core-penumbra quantification in neonatal ischemic brain injury. J. Cereb. Blood Flow Metab. 32(12), 2161–2170 (2012)

    Article  Google Scholar 

  6. de Haan, B., Clas, P., Juenger, H., Wilke, M., Karnath, H.O.: Fast semi-automated lesion demarcation in stroke. NeuroImage Clin. 9, 69–74 (2015). http://www.sciencedirect.com/science/article/pii/S2213158215001199

    Article  Google Scholar 

  7. Hevia-Montiel, N., Jimenez-Alaniz, J., Medina-Banuelos, V., et al.: Robust nonparametric segmentation of infarct lesion from diffusion-weighted MR images. IEEE EMBS 2007, 2102–2105 (2007)

    Google Scholar 

  8. Joy, J.E., Johnston, R.B.: Multiple Sclerosis: Current Status and Strategies for the Future. National Academies Press, Washington (2001). http://www.ncbi.nlm.nih.gov/books/NBK222399/

    Google Scholar 

  9. Kaus, M.R., Warfield, S.K., Nabavi, A., et al.: Automated segmentation of MR images of brain tumors. Radiology 218(2), 586–591 (2001)

    Article  Google Scholar 

  10. 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)

    Article  Google Scholar 

  11. Krämer, U.M., Solbakk, A.K., Funderud, I., et al.: The role of the lateral prefrontal cortex in inhibitory motor control. Cortex 49(3), 837–849 (2013)

    Article  Google Scholar 

  12. La Mantia, L., Di Pietrantonj, C., Rovaris, M., et al.: Interferons-beta versus glatiramer acetate for relapsing-remitting multiple sclerosis. Cochrane Database Syst. Rev. 7, CD009333 (2014). http://www.ncbi.nlm.nih.gov/pubmed/25062935

    Google Scholar 

  13. Lansberg, M.G., Straka, M., Kemp, S., et al.: MRI profile and response to endovascular reperfusion after stroke (DEFUSE 2): a prospective cohort study. Lancet. Neurol. 11(10), 860–867 (2012). http://www.thelancet.com/article/S147444221270203X/fulltext

    Article  Google Scholar 

  14. Likar, B., Viergever, M.A., Pernus, F.: Retrospective correction of MR intensity inhomogeneity by information minimization. IEEE Trans. Med. Imag. 20(12), 1398–1410 (2001)

    Article  Google Scholar 

  15. Maier, O.: MedPy. https://pypi.python.org/pypi/MedPy. Accessed 29 March 2015

  16. Maier, O., Wilms, M., et al.: Extra tree forests for sub-acute ischemic stroke lesion segmentation in MR sequences. J. Neurosci. Methods 240, 89–100 (2015)

    Article  Google Scholar 

  17. Menze, B., Jakab, A., Bauer, S., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)

    Article  Google Scholar 

  18. Mishra, N.K., Albers, G.W., Christensen, S., et al.: Comparison of magnetic resonance imaging mismatch criteria to select patients for endovascular stroke therapy. Stroke 45(5), 1369–1374 (2014). http://stroke.ahajournals.org/content/45/5/1369.full

    Article  Google Scholar 

  19. Mitra, J., Bourgeat, P., Fripp, J., et al.: Lesion segmentation from multimodal MRI using random forest following ischemic stroke. Neuroimage 98, 324–335 (2014)

    Article  Google Scholar 

  20. Nyul, L., Udupa, J., Zhang, X.: New variants of a method of MRI scale standardization. IEEE Trans. Med. Imaging 19(2), 143–150 (2000)

    Article  Google Scholar 

  21. Olivot, J.M., Mlynash, M., Thijs, V.N., et al.: Optimal Tmax threshold for predicting penumbral tissue in acute stroke. Stroke 40(2), 469–475 (2009). http://stroke.ahajournals.org/content/40/2/469.abstract

    Article  Google Scholar 

  22. Pedregosa, F., Varoquaux, G., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  23. Polman, C.H., Reingold, S.C., Banwell, B., et al.: Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann. Neurol. 69(2), 292–302 (2011). http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3084507&tool=pmcentrez&rendertype=abstract

  24. 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. A critical appraisal. Neuroimage Clin. 1(1), 164–178 (2012)

    Article  Google Scholar 

  25. 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(4–3), 1253–1266 (2008)

    Article  Google Scholar 

  26. Wilke, M., de Haan, B., Juenger, H., Karnath, H.O.: Manual, semi-automated, and automated delineation of chronic brain lesions: a comparison of methods. Neuroimage 56(4), 2038–2046 (2011)

    Article  Google Scholar 

  27. Zikic, D., Glocker, B., Konukoglu, E., Criminisi, A., Demiralp, C., Shotton, J., Thomas, O.M., Das, T., Jena, R., Price, S.J.: Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 369–376. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Medical Informatics, Universität zu Lübeck, Lübeck, Germany

    Oskar Maier, Matthias Wilms & Heinz Handels

  2. Graduate School for Computing in Medicine and Life Sciences, Universität zu Lübeck, Lübeck, Germany

    Oskar Maier

Authors
  1. Oskar Maier
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  2. Matthias Wilms
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  3. Heinz Handels
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Corresponding author

Correspondence to Oskar Maier .

Editor information

Editors and Affiliations

  1. Istituto Italiano di Tecnologia (IIT), Genova, Italy

    Alessandro Crimi

  2. TU München, Computer Science, München, Germany

    Bjoern Menze

  3. Medical Informatics, University of Lübeck, Lübeck, Germany

    Oskar Maier

  4. Surgical Technology and Biomechanics, Universität Bern, Bern, Switzerland

    Mauricio Reyes

  5. Medical Informatics, University of Lübeck, Lübeck, Germany

    Heinz Handels

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Maier, O., Wilms, M., Handels, H. (2016). Image Features for Brain Lesion Segmentation Using Random Forests. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2015. Lecture Notes in Computer Science(), vol 9556. Springer, Cham. https://doi.org/10.1007/978-3-319-30858-6_11

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  • DOI: https://doi.org/10.1007/978-3-319-30858-6_11

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30857-9

  • Online ISBN: 978-3-319-30858-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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