Deformable registration of brain tumor images via a statistical model of tumor-induced deformation

Ashraf Mohamed; Dinggang Shen; Christos Davatzikos

doi:10.1007/11566489_33

Deformable registration of brain tumor images via a statistical model of tumor-induced deformation

Ashraf Mohamed^*
, Dinggang Shen
, Christos Davatzikos

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Citations (Scopus)

Abstract

An approach to deformable registration of three-dimensional brain tumor images to a normal brain atlas is presented. The approach involves the integration of three components: a biomechanical model of tumor mass-effect, a statistical approach to estimate the model's parameters, and a deformable image registration method. Statistical properties of the desired deformation map are first obtained through tumor masseffect simulations on normal brain images. This map is decomposed into the sum of two components in orthogonal subspaces, one representing inter-individual differences, and the other involving tumor-induced deformation. For a new tumor case, a partial observation of the desired deformation map is obtained via deformable image registration and is decomposed into the aforementioned spaces in order to estimate the mass-effect model parameters. Using this estimate, a simulation of tumor mass-effect is performed on the atlas to generate an image that is more similar to brain tumor image, thereby facilitating the atlas registration process. Results for a real and a simulated tumor case indicate significant reduction in the registration error due to the presented approach as compared to the direct use of deformable image registration.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005 - 8th International Conference, Proceedings
Pages	263-270
Number of pages	8
DOIs	https://doi.org/10.1007/11566489_33
Publication status	Published - 2005
Event	8th International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005 - Palm Springs, CA, United States Duration: 2005 Oct 26 → 2005 Oct 29

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3750 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	8th International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005
Country/Territory	United States
City	Palm Springs, CA
Period	05/10/26 → 05/10/29

Bibliographical note

Funding Information:
The authors thank Dr. Nick Fox at the University College London, UK, for providing the tumor patient’s images. We also thank Xiaoying Wu at the Section of Biomedical Image Analysis at the University of Pennsylvania for her help in processing the used data. This work was supported in part by the National Science Foundation under Engineering Research Center Grant EEC9731478, and by the National Institutes of Health Grant R01NS42645.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/11566489_33

Cite this

Mohamed, A., Shen, D., & Davatzikos, C. (2005). Deformable registration of brain tumor images via a statistical model of tumor-induced deformation. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005 - 8th International Conference, Proceedings (pp. 263-270). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3750 LNCS). https://doi.org/10.1007/11566489_33

Deformable registration of brain tumor images via a statistical model of tumor-induced deformation. / Mohamed, Ashraf; Shen, Dinggang; Davatzikos, Christos.
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005 - 8th International Conference, Proceedings. 2005. p. 263-270 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3750 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Mohamed, A, Shen, D & Davatzikos, C 2005, Deformable registration of brain tumor images via a statistical model of tumor-induced deformation. in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005 - 8th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3750 LNCS, pp. 263-270, 8th International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005, Palm Springs, CA, United States, 05/10/26. https://doi.org/10.1007/11566489_33

Mohamed A, Shen D, Davatzikos C. Deformable registration of brain tumor images via a statistical model of tumor-induced deformation. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005 - 8th International Conference, Proceedings. 2005. p. 263-270. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11566489_33

Mohamed, Ashraf ; Shen, Dinggang ; Davatzikos, Christos. / Deformable registration of brain tumor images via a statistical model of tumor-induced deformation. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005 - 8th International Conference, Proceedings. 2005. pp. 263-270 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "An approach to deformable registration of three-dimensional brain tumor images to a normal brain atlas is presented. The approach involves the integration of three components: a biomechanical model of tumor mass-effect, a statistical approach to estimate the model's parameters, and a deformable image registration method. Statistical properties of the desired deformation map are first obtained through tumor masseffect simulations on normal brain images. This map is decomposed into the sum of two components in orthogonal subspaces, one representing inter-individual differences, and the other involving tumor-induced deformation. For a new tumor case, a partial observation of the desired deformation map is obtained via deformable image registration and is decomposed into the aforementioned spaces in order to estimate the mass-effect model parameters. Using this estimate, a simulation of tumor mass-effect is performed on the atlas to generate an image that is more similar to brain tumor image, thereby facilitating the atlas registration process. Results for a real and a simulated tumor case indicate significant reduction in the registration error due to the presented approach as compared to the direct use of deformable image registration.",

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AB - An approach to deformable registration of three-dimensional brain tumor images to a normal brain atlas is presented. The approach involves the integration of three components: a biomechanical model of tumor mass-effect, a statistical approach to estimate the model's parameters, and a deformable image registration method. Statistical properties of the desired deformation map are first obtained through tumor masseffect simulations on normal brain images. This map is decomposed into the sum of two components in orthogonal subspaces, one representing inter-individual differences, and the other involving tumor-induced deformation. For a new tumor case, a partial observation of the desired deformation map is obtained via deformable image registration and is decomposed into the aforementioned spaces in order to estimate the mass-effect model parameters. Using this estimate, a simulation of tumor mass-effect is performed on the atlas to generate an image that is more similar to brain tumor image, thereby facilitating the atlas registration process. Results for a real and a simulated tumor case indicate significant reduction in the registration error due to the presented approach as compared to the direct use of deformable image registration.

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Deformable registration of brain tumor images via a statistical model of tumor-induced deformation

Abstract

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