Nonrigid 3D brain registration using intensity/feature information

Christine DeLorenzo; Xenophon Papademetris; Kun Wu; Kenneth P. Vives; Dennis Spencer; James S. Duncan

doi:10.1007/11866565_114

Nonrigid 3D brain registration using intensity/feature information

Christine DeLorenzo
, Xenophon Papademetris
, Kun Wu
, Kenneth P. Vives
, Dennis Spencer
, James S. Duncan

Psychiatry

Yale University

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

14 Scopus citations

Abstract

The brain deforms non-rigidly during neurosurgery, preventing preoperatively acquired images from accurately depicting the intraoperative brain. If the deformed brain surface can be detected, biomechanical models can be applied to calculate the resulting volumetric deformation. The reliability of this volumetric calculation is dependent on the accuracy of the surface detection. This work presents a surface tracking algorithm which relies on Bayesian analysis to track cortical surface movement. The inputs to the model are 3D preoperative brain images and intraoperative stereo camera images. The addition of a camera calibration optimization term creates a more robust model, capable of tracking the cortical surface in the presence of camera calibration error.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006 - 9th International Conference, Proceedings
Publisher	Springer Verlag
Pages	932-939
Number of pages	8
ISBN (Print)	3540447075, 9783540447078
DOIs	https://doi.org/10.1007/11866565_114
State	Published - 2006
Event	9th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006 - Copenhagen, Denmark Duration: Oct 1 2006 → Oct 6 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4190 LNCS - I
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006
Country/Territory	Denmark
City	Copenhagen
Period	10/1/06 → 10/6/06

Access to Document

10.1007/11866565_114

Cite this

DeLorenzo, C., Papademetris, X., Wu, K., Vives, K. P., Spencer, D., & Duncan, J. S. (2006). Nonrigid 3D brain registration using intensity/feature information. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006 - 9th International Conference, Proceedings (pp. 932-939). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4190 LNCS - I). Springer Verlag. https://doi.org/10.1007/11866565_114

DeLorenzo, Christine ; Papademetris, Xenophon ; Wu, Kun et al. / Nonrigid 3D brain registration using intensity/feature information. Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006 - 9th International Conference, Proceedings. Springer Verlag, 2006. pp. 932-939 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{a146eea56dba43af8df698f6da75c463,

title = "Nonrigid 3D brain registration using intensity/feature information",

abstract = "The brain deforms non-rigidly during neurosurgery, preventing preoperatively acquired images from accurately depicting the intraoperative brain. If the deformed brain surface can be detected, biomechanical models can be applied to calculate the resulting volumetric deformation. The reliability of this volumetric calculation is dependent on the accuracy of the surface detection. This work presents a surface tracking algorithm which relies on Bayesian analysis to track cortical surface movement. The inputs to the model are 3D preoperative brain images and intraoperative stereo camera images. The addition of a camera calibration optimization term creates a more robust model, capable of tracking the cortical surface in the presence of camera calibration error.",

author = "Christine DeLorenzo and Xenophon Papademetris and Kun Wu and Vives, \{Kenneth P.\} and Dennis Spencer and Duncan, \{James S.\}",

year = "2006",

doi = "10.1007/11866565\_114",

language = "English",

isbn = "3540447075",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "932--939",

booktitle = "Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006 - 9th International Conference, Proceedings",

note = "9th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006 ; Conference date: 01-10-2006 Through 06-10-2006",

}

DeLorenzo, C, Papademetris, X, Wu, K, Vives, KP, Spencer, D & Duncan, JS 2006, Nonrigid 3D brain registration using intensity/feature information. in Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006 - 9th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4190 LNCS - I, Springer Verlag, pp. 932-939, 9th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006, Copenhagen, Denmark, 10/1/06. https://doi.org/10.1007/11866565_114

Nonrigid 3D brain registration using intensity/feature information. / DeLorenzo, Christine; Papademetris, Xenophon; Wu, Kun et al.
Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006 - 9th International Conference, Proceedings. Springer Verlag, 2006. p. 932-939 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4190 LNCS - I).

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

TY - GEN

T1 - Nonrigid 3D brain registration using intensity/feature information

AU - DeLorenzo, Christine

AU - Papademetris, Xenophon

AU - Wu, Kun

AU - Vives, Kenneth P.

AU - Spencer, Dennis

AU - Duncan, James S.

PY - 2006

Y1 - 2006

N2 - The brain deforms non-rigidly during neurosurgery, preventing preoperatively acquired images from accurately depicting the intraoperative brain. If the deformed brain surface can be detected, biomechanical models can be applied to calculate the resulting volumetric deformation. The reliability of this volumetric calculation is dependent on the accuracy of the surface detection. This work presents a surface tracking algorithm which relies on Bayesian analysis to track cortical surface movement. The inputs to the model are 3D preoperative brain images and intraoperative stereo camera images. The addition of a camera calibration optimization term creates a more robust model, capable of tracking the cortical surface in the presence of camera calibration error.

AB - The brain deforms non-rigidly during neurosurgery, preventing preoperatively acquired images from accurately depicting the intraoperative brain. If the deformed brain surface can be detected, biomechanical models can be applied to calculate the resulting volumetric deformation. The reliability of this volumetric calculation is dependent on the accuracy of the surface detection. This work presents a surface tracking algorithm which relies on Bayesian analysis to track cortical surface movement. The inputs to the model are 3D preoperative brain images and intraoperative stereo camera images. The addition of a camera calibration optimization term creates a more robust model, capable of tracking the cortical surface in the presence of camera calibration error.

UR - https://www.scopus.com/pages/publications/84883835095

U2 - 10.1007/11866565_114

DO - 10.1007/11866565_114

M3 - Conference contribution

C2 - 17354980

AN - SCOPUS:84883835095

SN - 3540447075

SN - 9783540447078

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 932

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BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006 - 9th International Conference, Proceedings

PB - Springer Verlag

T2 - 9th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006

Y2 - 1 October 2006 through 6 October 2006

ER -

DeLorenzo C, Papademetris X, Wu K, Vives KP, Spencer D, Duncan JS. Nonrigid 3D brain registration using intensity/feature information. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006 - 9th International Conference, Proceedings. Springer Verlag. 2006. p. 932-939. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11866565_114

Nonrigid 3D brain registration using intensity/feature information

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