- Esther Alberts18,19,23,
- Guillaume Charpiat20,
- Yuliya Tarabalka21,
- Thomas Huber18,
- Marc-André Weber22,
- Jan Bauer18,
- Claus Zimmer18 &
- …
- Bjoern H. Menze19,23
Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 9556))
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Abstract
Brain tumor segmentation and brain tumor growth assessment are inter-dependent and benefit from a joint evaluation. Starting from a generative model for multimodal brain tumor segmentation, we make use of a nonparametric growth model that is implemented as a conditional random field (CRF) including directed links with infinite weight in order to incorporate growth and inclusion constraints, reflecting our prior belief on tumor occurrence in the different image modalities. In this study, we validate this model to obtain brain tumor segmentations and volumetry in longitudinal image data. Moreover, we use the model to develop a probabilistic framework for estimating the likelihood of disease progression, i.e. tumor regrowth, after therapy. We present experiments for longitudinal image sequences with
,
,
andflair images, acquired for ten patients with low and high grade gliomas.
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References
Menze, B.H., Jakab, A., Bauer, S., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imag.34(10), 1993–2024 (2015)
Bauer, S., Tessier, J., Krieter, O., Nolte, L.P., Reyes, M.: Integrated spatio-temporal segmentation of longitudinal brain tumor imaging studies. In: Menze, B., Langs, G., Montillo, A., Kelm, M., Müller, H., Tu, Z. (eds.) MCV 2013. LNCS, vol. 8331, pp. 74–83. Springer, Switzerland (2013)
Tarabalka, Y., et al.: Spatio-temporal video segmentation with shape growth or shrinkage constraint. IEEE Trans. Image Process.23(9), 3829–3840 (2014)
Angelini, E.D., Clatz, O., Mandonnet, E., et al.: Glioma dynamics and computational models: a review. Curr. Med. Imaging Rev.3, 262–276 (2007)
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)
Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimisation in vision. IEEE Trans. Pattern Anal. Mach. Intell.26(9), 1124–1137 (2004)
Candemir, S., Akgül, Y.S.: Adaptive regularization parameter for graph cut segmentation. In: Campilho, A., Kamel, M. (eds.) ICIAR 2010. LNCS, vol. 6111, pp. 117–126. Springer, Heidelberg (2010)
Kohli, P., Torr, P.H.S.: Measuring uncertainty in graph cut solutions. Comput. Vis. Image Underst.112, 30–38 (2008)
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)
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Authors and Affiliations
Neuroradiology, Klinikum Rechts der Isar, TU München, Munich, Germany
Esther Alberts, Thomas Huber, Jan Bauer & Claus Zimmer
Department of Computer Science, TU München, Munich, Germany
Esther Alberts & Bjoern H. Menze
TAO Research Project, Inria Saclay, Palaiseau, France
Guillaume Charpiat
Titane Research Project, Inria Sophia-Antipolis, Valbonne, France
Yuliya Tarabalka
Diagnostic and Interventional Radiology, University of Heidelberg, Heidelberg, Germany
Marc-André Weber
Institute for Advanced Study, TU München, Munich, Germany
Esther Alberts & Bjoern H. Menze
- Esther Alberts
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- Guillaume Charpiat
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- Thomas Huber
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- Marc-André Weber
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- Jan Bauer
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- Claus Zimmer
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- Bjoern H. Menze
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Correspondence toEsther Alberts.
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Editors and Affiliations
Istituto Italiano di Tecnologia (IIT), Genova, Italy
Alessandro Crimi
TU München, Computer Science, München, Germany
Bjoern Menze
Medical Informatics, University of Lübeck, Lübeck, Germany
Oskar Maier
Surgical Technology and Biomechanics, Universität Bern, Bern, Switzerland
Mauricio Reyes
Medical Informatics, University of Lübeck, Lübeck, Germany
Heinz Handels
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Alberts, E.et al. (2016). A Nonparametric Growth Model for Brain Tumor Segmentation in Longitudinal MR Sequences. 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_7
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