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Classification Forests for Semantic Segmentation of Brain Lesions in Multi-channel MRI

  • E. Geremia
  • D. Zikic
  • O. Clatz
  • B. H. Menze
  • B. Glocker
  • E. Konukoglu
  • J. Shotton
  • O. M. Thomas
  • S. J. Price
  • T. Das
  • R. Jena
  • N. Ayache
  • A. Criminisi
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

Classification forests, as discussed in Chap.  4, present a series of advantages which make them a good choice for applications in medical image analysis. Classification forests are inherent multi-class classifiers (which allows for e.g. the simultaneous segmentation of different tissues), have good generalization properties (which is important as training data are often scarce in medical applications), and are able to deal with very high-dimensional feature spaces (which permits the use of long-range, context-rich features). In this chapter we demonstrate how classification forests can be used as a basic building block to develop state of the art systems for medical image analysis in two challenging applications. Given 3D multi-channel magnetic resonance images (MRI) as input we use forests for: (i) the tissue-specific segmentation of high-grade brain tumors (namely glioblastoma tumors), and (ii) the segmentation of multiple sclerosis (MS) lesions.

Keywords

Diffusion Tensor Imaging Gaussian Mixture Model Necrotic Core Multiple Sclerosis Lesion Semantic Context 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 18.
    Barnett GH (ed) (2007) High-grade gliomas. Springer, Berlin Google Scholar
  2. 20.
    Bauer S, Nolte L-P, Reyes M (2011) Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization. In: Fichtinger G, Martel A, Peters T (eds) Proc medical image computing and computer assisted intervention (MICCAI). LNCS, vol 6893. Springer, Berlin Google Scholar
  3. 55.
    Calabresi P (2007) Multiple sclerosis and demyelinating conditions of the central nervous system. In: Cecil medicine. Saunders Elsevier, Philadelphia Google Scholar
  4. 72.
    Corso JJ, Sharon E, Dube S, El-saden S, Sinha U, Yuille A (2008) Efficient multilevel brain tumor segmentation with integrated Bayesian model classification. Trans Med Imaging 27(5) Google Scholar
  5. 76.
    Criminisi A, Sharp T, Blake A (2008) GeoS: geodesic image segmentation. In: Proc European conf on computer vision (ECCV). Springer, Berlin Google Scholar
  6. 98.
    Evans AC, Collins DL, Mills SR, Brown ED, Kelly RL, Peters TM (1993) 3D statistical neuroanatomical models from 305 MRI volumes. In: IEEE-nuclear science symposium and medical imaging conference Google Scholar
  7. 126.
    Geremia E, Clatz O, Menze BH, Konukoglu E, Criminisi A, Ayache N (2011) Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance. NeuroImage Google Scholar
  8. 136.
    Gooya A, Pohl KM, Bilello M, Biros G, Davatzikos C (2011) Joint segmentation and deformable registration of brain scans guided by a tumor growth model. In: Proc medical image computing and computer assisted intervention (MICCAI) Google Scholar
  9. 137.
    Gorlitz L, Menze BH, Weber M-A, Kelm BM, Hamprecht FA (2007) Semi-supervised tumor detection in magnetic resonance spectroscopic images using discriminative random fields. In: Proc annual symposium of the German association for pattern recognition (DAGM) Google Scholar
  10. 167.
    Ho S, Bullitt E, Gerig G (2002) Level-set evolution with region competition: automatic 3-D segmentation of brain tumors. In: Proc intl conf on pattern recognition (ICPR) Google Scholar
  11. 205.
    Lee CH, Wang S, Murtha A, Brown M, Greiner R (2008) Segmenting brain tumors using pseudo-conditional random fields. In: Proc medical image computing and computer assisted intervention (MICCAI) Google Scholar
  12. 245.
    Menze BH, Leemput KV, Lashkari D, Weber M-A, Ayache N, Golland P (2010) A generative model for brain tumor segmentation in multi-modal images. In: Proc medical image computing and computer assisted intervention (MICCAI) Google Scholar
  13. 294.
    Popuri K, Cobzas D, Murtha A, Jägersand M (2011) 3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set. Int J Comput Assisted Radiol Surg Google Scholar
  14. 297.
    Prastawa M, Bullitt E, Ho S, Gerig G (2004) A brain tumor segmentation framework based on outlier detection. Med Image Anal Google Scholar
  15. 299.
    Price SJ, Peña A, Burnet NG, Jena R, Green HAL, Carpenter TA, Pickard JD, Gillard JH (2004) Tissue signature characterisation of diffusion tensor abnormalities in cerebral gliomas. Eur Radiol 14 Google Scholar
  16. 300.
    Prima S, Ayache N, Barrick T, Roberts N (2001) Maximum likelihood estimation of the bias field in MR brain images: investigating different modelings of the imaging process. In: Proc medical image computing and computer assisted intervention (MICCAI). LNCS, vol 2208. Springer, Berlin Google Scholar
  17. 301.
    Prima S, Ourselin S, Ayache N (2002) Computation of the mid-sagittal plane in 3D brain images. Trans Med Imaging 21(2) Google Scholar
  18. 308.
    Rey D (2002) Détection et quantification de processus évolutifs dans des images médicales tridimensionnelles : application à la sclérose en plaques. Thèse de sciences, Université de Nice Sophia-Antipolis, October (in French) Google Scholar
  19. 322.
    Schmidt M, Levner I, Greiner R, Murtha A, Bistriz A (2005) Segmenting brain tumors using alignment-based features. In: ICMLA Google Scholar
  20. 342.
    Shotton J, Winn JM, Rother C, Criminisi A (2009) TextonBoost for image understanding: multi-class object recognition and segmentation by jointly modeling texture, layout, and context. Int J Comput Vis 81(1) Google Scholar
  21. 350.
    Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp Google Scholar
  22. 353.
    Souplet J-C, Lebrun C, Ayache N, Malandain G (2008) An automatic segmentation of T2-FLAIR multiple sclerosis lesions. In: The MIDAS journal—MS lesion segmentation (MICCAI 2008 workshop) Google Scholar
  23. 358.
    Styner M, Lee J, Chin B, Chin MS, Commowick O, Tran H, Markovic-Plese S, Jewells V, Warfield SK (2008) 3D segmentation in the clinic: a grand challenge II: MS lesion segmentation. MIDAS J Google Scholar
  24. 375.
    Tu Z, Bai X (2010) Auto-context and its application to high-level vision tasks and 3D brain image segmentation. IEEE Trans Pattern Anal Mach Intell 32(10) Google Scholar
  25. 385.
    Verma R, Zacharaki EI, Ou Y, Cai H, Chawla S, Lee A-K, Melhem ER, Wolf R, Davatzikos C (2008) Multi-parametric tissue characterization of brain neoplasm and their recurrence using pattern classification of MR images. Acad Radiol 15(8) Google Scholar
  26. 399.
    Wels M, Carneiro G, Aplas A, Huber M, Comaniciu D, Hornegger J (2008) A discriminative model-constrained graph-cuts approach to fully automated pediatric brain tumor segmentation in 3D MRI. In: Proc medical image computing and computer assisted intervention (MICCAI) Google Scholar
  27. 400.
    Wen PY, Macdonald DR, Reardon DA, Cloughesy TF, Sorensen AG, Galanis E, Degroot J, Wick W, Gilbert MR, Lassman AB, Tsien C, Mikkelsen T, Wong ET, Chamberlain MC, Stupp R, Lamborn KR, Vogelbaum MA, van den Bent MJ, Chang SM (2010) Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. Am J Neuroradiol Google Scholar
  28. 428.
    Zikic D, Glocker B, Konukoglu E, Criminisi A, Demiralp C, Shotton J, Thomas OM, Das T, Jena R, Price SJ (2012) Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR. In: Proc medical image computing and computer assisted intervention (MICCAI) Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • E. Geremia
    • 1
  • D. Zikic
    • 2
  • O. Clatz
    • 1
  • B. H. Menze
    • 3
  • B. Glocker
    • 2
  • E. Konukoglu
    • 2
  • J. Shotton
    • 2
  • O. M. Thomas
    • 4
  • S. J. Price
    • 4
  • T. Das
    • 4
  • R. Jena
    • 4
  • N. Ayache
    • 1
  • A. Criminisi
    • 2
  1. 1.Asclepios Research ProjectInriaSophia-AntipolisFrance
  2. 2.Microsoft Research Ltd.CambridgeUK
  3. 3.ETH ZurichZurichSwitzerland
  4. 4.Cambridge University HospitalsCambridgeUK

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