Abstract
A quantitative analysis of brain tumors is an important factor that can have direct impact on a patient’s prognosis and treatment. In order to achieve clinical relevance, reproducibility and especially accuracy of a proposed method have to be tested. We propose a framework for the generation of realistic digital phantoms of brain tumors of known volumes and their incorporation into an MR dataset of a healthy volunteer. Deformations that occur due to tumor growth inside the brain are simulated by means of a biomechanical model. Furthermore, a model for the amount of edema at each voxel is included as well as a simulation of contrast enhancement, which provides us with an additional characterization of the tumor. A “ground truth” is generally not available for brain tumors. Our proposed framework provides a flexible tool to generate representative datasets with known ground truth, which is essential for the validation and comparison of current and new quantitative approaches. Experiments are carried out using a semi-automated volumetry approach for a set of generated tumor datasets.
Chapter PDF
Similar content being viewed by others
Keywords
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
Osborn, A.G., Tong, K.A.: Handbook of Neuroradiology: Brain and Skull, 2nd edn. Mosby- Year Book, Inc., Missouri (1991)
Kaus, M., Warfield, S.K., Nabavi, A., et al.: Automated Segmentation of MR Images of Brain Tumors. Radiology 218(2), 586–591 (2001)
Moonis, G., Liu, J., Udupa, J.K., Hackney, D.B.: Estimation of Tumor Volume with Fuzzy- Connectedness Segmentation of MR Images. Am. J. Neuroradiol. 23(3), 356–363 (2002)
Guyon, J.-P., Foskey, M., Kim, J., et al.: VETOT, Volume Estimation and Tracking Over Time: Framework and Validation. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2879, pp. 142–149. Springer, Heidelberg (2003)
Prastawa, M., Bullitt, E., Moon, N., et al.: Automatic Brain Tumor Segmentation by Subject Specific Modification of Atlas Priors. Acad. Radiol. 10, 1341–1348 (2003)
Tofts, P.S., Barker, G.J., Filippi, M., Gawne-Cain, M., Lai, M.: An oblique cylinder contrast adjusted (OCCA) phantom to measure the accuracy of MRI brain lesion volume estimation schemes in multiple sclerosis. J. Magn. Reson. Imaging 15(2), 183–192 (1997)
Collins, D., Zijdenbos, A., Kollokian, V., et al.: Design and Construction of a Realistic Digital Brain Phantom. IEEE TMI 17(5), 463–468 (1998)
Rexilius, J., Hahn, H.K., Bourquain, H., Peitgen, H.-O.: Ground truth in MS lesion volumetry – A phantom study. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2879, pp. 546–553. Springer, Heidelberg (2003)
Kansal, A.R., Torquato, S., Harsh, G.R., et al.: Simulated Brain Tumor Growth Dynamics Using a Three-Dimensional Cellular Automaton. J. Theor. Biol. 203(4), 367–382 (2000)
Wasserman, R., Acharya, R., Sibata, C., Shin, K.H.: A Patient-Specific In Vivo Tumor Model. Math. Biosci. 136(2), 111–140 (1996)
Ferrant, M., Nabavi, A., Macq, B., et al.: Registration of 3D interoperative MR images of the brain using a finite element biomechanical model. IEEE TMI 20(12), 1384–1397 (2001)
Rexilius, J., Warfield, S.K., Guttmann, C.R.G., et al.: A Novel Nonrigid Registration Algorithm and Applications. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 923–931. Springer, Heidelberg (2001)
Hahn, H.K., Peitgen, H.-O.: The Skull Stripping Problem in MRI Solved by a Single 3D Watershed Transform. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 134–143. Springer, Heidelberg (2000)
Zienkewickz, O.C., Taylor, R.L.: The Finite Element Method. McGraw Hill Book Co., New York (1987)
Modersitzki, J.: Numerical Methods for Image Registration. Oxford University Press, Oxford (2004)
Soille, P.: Morphological Image Analysis: Principles and Applications, 2nd edn. Springer, Berlin (2003)
Me VisLab 1.0, (c) MeVis gGmbH (2004), Available at: http://www.mevislab.de
Noe, A., Gee, J.C.: Partial Volume Segmentation of Cerebral MRI Scans with Mixture Model Clustering. In: Insana, M.F., Leahy, R.M. (eds.) IPMI 2001. LNCS, vol. 2082, pp. 423–430. Springer, Heidelberg (2001)
Tofts, P.S., Kermode, A.G.: Measurement of the Blood-Brain Barrier Permeability and Leak Space Using Dynamic MR Imaging. 1. Fundamental Concepts. Magn. Reson. Med. 17, 357–367 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rexilius, J. et al. (2004). A Framework for the Generation of Realistic Brain Tumor Phantoms and Applications. In: Barillot, C., Haynor, D.R., Hellier, P. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004. MICCAI 2004. Lecture Notes in Computer Science, vol 3217. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30136-3_31
Download citation
DOI: https://doi.org/10.1007/978-3-540-30136-3_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22977-3
Online ISBN: 978-3-540-30136-3
eBook Packages: Springer Book Archive