Abstract
We present a method for automatic segmentation of high-grade gliomas and their subregions from multi-channel MR images. Besides segmenting the gross tumor, we also differentiate between active cells, necrotic core, and edema. Our discriminative approach is based on decision forests using context-aware spatial features, and integrates a generative model of tissue appearance, by using the probabilities obtained by tissue-specific Gaussian mixture models as additional input for the forest. Our method classifies the individual tissue types simultaneously, which has the potential to simplify the classification task. The approach is computationally efficient and of low model complexity. The validation is performed on a labeled database of 40 multi-channel MR images, including DTI. We assess the effects of using DTI, and varying the amount of training data. Our segmentation results are highly accurate, and compare favorably to the state of the art.
Chapter PDF
Similar content being viewed by others
Keywords
- Gaussian Mixture Model
- Necrotic Core
- Magnetic Resonance Spectroscopic Image
- Discriminative Approach
- Tumor Growth Model
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
Sup. material, http://research.microsoft.com/apps/pubs/default.aspx?id=164382
Bauer, S., Nolte, L.-P., Reyes, M.: 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.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 354–361. Springer, Heidelberg (2011)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Corso, J.J., Sharon, E., Dube, S., El-saden, S., Sinha, U., Yuille, A.: Efficient multilevel brain tumor segmentation with integrated bayesian model classification. IEEE Trans. Medical Imaging 27(5) (2008)
Criminisi, A., Shotton, J., Konukoglu, E.: Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. FnT Computer Graphics and Vision (2012)
Gooya, A., Pohl, K.M., Bilello, M., Biros, G., Davatzikos, C.: Joint Segmentation and Deformable Registration of Brain Scans Guided by a Tumor Growth Model. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 532–540. Springer, Heidelberg (2011)
Görlitz, L., Menze, B.H., Weber, M.-A., Kelm, B.M., Hamprecht, F.A.: Semi-supervised Tumor Detection in Magnetic Resonance Spectroscopic Images Using Discriminative Random Fields. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 224–233. Springer, Heidelberg (2007)
Ho, S., Bullitt, E., Gerig, G.: Level-set evolution with region competition: automatic 3-D segmentation of brain tumors. In: ICPR (2002)
Kaus, M.R., Warfield, S.K., Nabavi, A., Black, P.M., Jolesz, F.A., Kikinis, R.: Automated segmentation of brain tumors. Radiology 218 (2001)
Menze, B.H., van Leemput, K., Lashkari, D., Weber, M.-A., Ayache, N., Golland, P.: A Generative Model for Brain Tumor Segmentation in Multi-Modal Images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part II. LNCS, vol. 6362, pp. 151–159. Springer, Heidelberg (2010)
Popuri, K., Cobzas, D., Murtha, A., Jägersand, M.: 3D variational brain tumor segmentation using dirichlet priors on a clustered feature set. Int. J. CARS (2011)
Prastawa, M., Bullitt, E., Ho, S., Gerig, G.: A brain tumor segmentation framework based on outlier detection. Medical Image Analysis (2004)
Schmidt, M., Levner, I., Greiner, R., Murtha, A., Bistriz, A.: Segmenting brain tumors using alignment-based features. In: Proc. of ICMLA (2005)
Smith, S.M.: Fast robust automated brain extraction. Hum. Br. Map. (2002)
Verma, R., Zacharaki, E.I., Ou, Y., Cai, H., Chawla, S., Lee, A.-K., Melhem, E.R., Wolf, R., Davatzikos, C.: Multi-parametric tissue characterisation of brain neoplasm and their recurrence using pattern classification of MR images. Acad. Radiol. 15(8) (2008)
Wels, M., Carneiro, G., Aplas, A., Huber, M., Hornegger, J., Comaniciu, D.: A Discriminative Model-Constrained Graph Cuts Approach to Fully Automated Pediatric Brain Tumor Segmentation in 3-D MRI. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 67–75. Springer, Heidelberg (2008)
Wen, P.Y., Macdonald, D.R., Reardon, D.A., Cloughesy, T.F., Sorensen, A.G., Galanis, E., Degroot, J., Wick, W., Gilbert, M.R., Lassman, A.B., Tsien, C., Mikkelsen, T., Wong, E.T., Chamberlain, M.C., Stupp, R., Lamborn, K.R., Vogelbaum, M.A., van den Bent, M.J., Chang, S.M.: Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. Am. J. Neuroradiol. (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zikic, D. et al. (2012). Decision Forests for Tissue-Specific Segmentation of High-Grade Gliomas in Multi-channel MR. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33454-2_46
Download citation
DOI: https://doi.org/10.1007/978-3-642-33454-2_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33453-5
Online ISBN: 978-3-642-33454-2
eBook Packages: Computer ScienceComputer Science (R0)