Abstract
In this paper, we present a fully automated generative method for brain tumor segmentation in multi-modal magnetic resonance images. The method is based on the type of generative model often used for segmenting healthy brain tissues, where tissues are modeled by Gaussian mixture models combined with a spatial atlas-based tissue prior. We extend this basic model with a tumor prior, which uses convolutional restricted Boltzmann machines (cRBMs) to model the shape of both tumor core and complete tumor, which includes edema and core. The cRBMs are trained on expert segmentations of training images, without the use of the intensity information in the training images. Experiments on public benchmark data of patients suffering from low- and high-grade gliomas show that the method performs well compared to current state-of-the-art methods, while not being tied to any specific imaging protocol.
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Tustison, N., et al.: ANTs and Árboles. In: Proceedings of the MICCAI-BRATS 2013 (2013)
Havaei, M., et al.: A convolutional neural network approach to brain tumor segmentation. In: Proceedings of the MICCAI-BRATS 2015 (2015)
Pereira, S., et al.: Deep convolutional neural networks for the segmentation of gliomas in multi-sequence MRI. In: Proceedings of the MICCAI-BRATS 2015 (2015)
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)
Kwon, D., Shinohara, R.T., Akbari, H., Davatzikos, C.: Combining generative models for multifocal glioma segmentation and registration. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part I. LNCS, vol. 8673, pp. 763–770. Springer, Heidelberg (2014)
Bakas, S., et al.: Segmentation of gliomas in multimodal magnetic resonance imaging volumes based on a hybrid generative-discriminative framework. In: Proceedings of the MICCAI-BRATS 2015 (2015)
Menze, B.H., et al.: Segmenting glioma in multi-modal images using a generative model for brain lesion segmentation. In: Proceedings of the MICCAI-BRATS 2012 (2012)
Haeck, T., et al.: Automated model-based segmentation of brain tumors in MR images. In: Proceedings of the MICCAI-BRATS 2015 (2015)
Leemput, V., et al.: Automated model-based tissue classification of MR images of the brain. IEEE Trans. Med. Imaging 18(10), 897–908 (1999)
Ashburner, J., et al.: Statistical Parametric Mapping. The Wellcome Department Cognitive Neurology, University College London, London, UK. http://www.fil.ion.ucl.ac.uk/spm/
Murphy, K.P.: Machine learning: a probabilistic perspective. MIT Press, Cambridge (2012)
Lee, H., et al.: Unsupervised learning of hierarchical representations with convolutional deep belief networks. Commun. ACM 54(10), 95–103 (2011)
Fischer, A., et al.: Training restricted boltzmann machines: an introduction. Pattern Recogn. 47(1), 25–39 (2014)
Melchior, J., et al.: How to center binary restricted boltzmann machines. arXiv preprint (2013). arXiv:1311.1354
Kistler, M., et al.: The virtual skeleton database: an open access repository for biomedical research and collaboration. J. Med. Internet Res 15(11), e245 (2013)
Acknowledgments
This research was supported by NIH NCRR (P41-RR14075), NIBIB (R01EB013565) and the Lundbeck Foundation (R141-2013-13117).
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Agn, M., Puonti, O., Rosenschöld, P.M.a., Law, I., Van Leemput, K. (2016). Brain Tumor Segmentation Using a Generative Model with an RBM Prior on Tumor Shape. 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_15
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DOI: https://doi.org/10.1007/978-3-319-30858-6_15
Publisher Name: Springer, Cham
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