Brain tumor cell density estimation from multi-modal MR images based on a synthetic tumor growth model

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7766 LNCS, Issue , 2013, Pages 273-282

  Geremia, Ezequiel ^a   Menze, Bjoern H ^a,b,c   Prastawa, Marcel ^d   Weber, M A ^e   Criminisi, Antonio ^f   Ayache, Nicholas ^a  

^a INRIA   (France)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c ETH ZURICH   (Switzerland)

^d UNIVERSITY OF UTAH   (United States)

^e UNIVERSITY HOSPITAL HEIDELBERG   (Germany)

^f MICROSOFT RESEARCH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DATA-DRIVEN LEARNING ALGORITHMS; EXPERT ANNOTATIONS; LOW-GRADE GLIOMAS; MULTI-MODAL IMAGE; QUANTITATIVE EVALUATION; REGRESSION FORESTS; STATE OF THE ART; TUMOR GROWTH MODELS;

DIGITAL STORAGE; LEARNING ALGORITHMS; MAGNETIC RESONANCE IMAGING; MEDICAL IMAGING;

TUMORS;

EID: 84875200185     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-36620-8_27     Document Type: Conference Paper

References (26)

1. Angelini, E.D., Delon, J., Bah, A.B., Capelle, L., Mandonnet, E.: Differential MRI analysis for quantification of low grade glioma growth. Medical Image Analysis 16, 114-126 (2012)
2. Cristini, V., Lowengrub, J.: Multiscale Modeling of Cancer: An Integrated Experimental and Mathematical Modeling Approach, pp. 185-205. Cambridge University Press (2010)
3. Deisboeck, T.S., Stamatakos, G.S.: Multiscale Cancer Modeling, pp. 359-406. CRC Press (2010)
4. Angelini, E.D., Clatz, O., Mandonnet, E., Konukoglu, E., Capelle, L., Duffau, H.: Glioma dynamics and computational models: A review of segmentation, registration, and in silico growth algorithms and their clinical applications. Cur. Med. Imag. Rev. 3, 262-276 (2007)
5. Menze, B.H., Van Leemput, K., Honkela, A., Konukoglu, E., Weber, M.-A., Ayache, N., Golland, P.: A Generative Approach for Image-Based Modeling of Tumor Growth. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 735-747. Springer, Heidelberg (2011)
6. Prastawa, M., Bullitt, E., Ho, S., Gerig, G.: A brain tumor segmentation framework based on outlier detection. Medical Image Analysis 8, 275-283 (2004)
7. Zou, K.H., Wells III, W.M., Kaus, M.R., Kikinis, R., Jolesz, F.A., Warfield, S.K.: Statistical Validation of Automated Probabilistic Segmentation against Composite Latent Expert Ground Truth in MR Imaging of Brain Tumors. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002, Part I. LNCS, vol. 2488, pp. 315-322. Springer, Heidelberg (2002)
8. 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)
9. Riklin-Raviv, T., Leemput, K.V., Menze, B.H., Wells, W.M., Golland, P.: Segmentation of image ensembles via latent atlases. Medical Image Analysis 14, 654-665 (2010)
10. Leemput, K.V., Maes, F., Vandermeulen, D., Colchester, A.C.F., Suetens, P.: Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans. Med. Imaging 20(8), 677-688 (2001)
11. Cabezas, M., Oliver, A., Lladó, X., Freixenet, J., Cuadra, M.B.: A review of atlas-based segmentation for magnetic resonance brain images. Comp. Meth. Prog. Biomed. 104, 158-164 (2011)
12. 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)
13. Zacharaki, E.I., Hogea, C.S., Shen, D., Biros, G., Davatzikos, C.: Non-diffeomorphic registration of brain tumor images by simulating tissue loss and tumor growth. NeuroImage 46, 762-774 (2009)
14. Lee, C.-H., Wang, S., Murtha, A., Brown, M.R.G., Greiner, R.: Segmenting Brain Tumors Using Pseudo-Conditional Random Fields. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 359-366. Springer, Heidelberg (2008)
15. 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)
16. Corso, J.J., Sharon, E., Dube, S., El-Saden, S., Sinha, U., Yuille, A.L.: Efficient multilevel brain tumor segmentation with integrated bayesian model classification. IEEE Transactions on Medical Imaging 27, 629-640 (2008)
17. Criminisi, A., Shotton, J., Robertson, D., Konukoglu, E.: Regression Forests for Efficient Anatomy Detection and Localization in CT Studies. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds.) MICCAI 2010. LNCS, vol. 6533, pp. 106-117. Springer, Heidelberg (2011)
18. Montillo, A., Shotton, J., Winn, J., Iglesias, J.E., Metaxas, D., Criminisi, A.: Entangled Decision Forests and Their Application for Semantic Segmentation of CT Images. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 184-196. Springer, Heidelberg (2011)
19. Gray, K.R., Aljabar, P., Heckemann, R.A., Hammers, A., Rueckert, D.: Random Forest-Based Manifold Learning for Classification of Imaging Data in Dementia. In: Suzuki, K., Wang, F., Shen, D., Yan, P. (eds.) MLMI 2011. LNCS, vol. 7009, pp. 159-166. Springer, Heidelberg (2011)
20. Geremia, E., Clatz, O., Menze, B.H., Konukoglu, E., Criminisi, A., Ayache, N.: Spatial decision forests for ms lesion segmentation in multi-channel magnetic resonance images. NeuroImage 57, 378-390 (2011)
21. Prastawa, M., Bullitt, E., Gerig, G.: Simulation of brain tumors in MR images for evaluation of segmentation efficacy. Medical Image Analysis 13, 297-311 (2009)
22. Criminisi, A., Shotton, J., Konukoglu, E.: Decision forests for classification, regression, density estimation, manifold learning and semi-supervised learning. Technical report, Microsoft (2011)
23. Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from single depth images. In: Proc CVPR, pp. 1297-1304 (2011)
24. Clatz, O., Sermesant, M., Bondiau, P.Y., Delingette, H.,Warfield, S.K., Malandain, G., Ayache, N.: Realistic simulation of the 3d growth of brain tumors in mr images coupling diffusion with mass effect. IEEE Transactions on Medical Imaging 24, 1334-1346 (2005)
25. Coltuc, D., Bolon, P., Chassery, J.M.: Exact histogram specification. IEEE TIP 15, 1143-1152 (2006)
26. Prima, S., Ourselin, S., Ayache, N.: Computation of the mid-sagittal plane in 3d brain images. IEEE Transactions on Medical Imaging 21, 122-138 (2002)