TY - GEN
T1 - A framework for predictive modeling of intra-operative deformations
T2 - 3rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2000
AU - Kyriacou, Stelios K.
AU - Shen, Dinggang
AU - Davatzikos, Christos
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2000.
PY - 2000
Y1 - 2000
N2 - Deformations that occur between pre-operative scans and the intra-operative setup can render pre-operative plans inaccurate or even unusable. It is therefore important to predict such deformations and account for them in pre-operative planning. This paper examines two different, yet related methodologies for this task, both of which collect statistical information from a training set in order to construct a predictive model. The first one examines the modes of co-variation between shape and deformation, and is therefore purely shape-based. The second approach additionally incorporates knowledge about the biomechanical properties of anatomical structures in constructing a predictive model. The two methods are tested on simulated training sets. Preliminary results show average errors of 9% (both methods) for a simulated dataset that had a moderate statistical variation and 36% (first method) and 23% (second method) for a dataset with a large statistical variation. Use of the above methodologies will hopefully lead to better clinical outcome by improving pre-operative plans.
AB - Deformations that occur between pre-operative scans and the intra-operative setup can render pre-operative plans inaccurate or even unusable. It is therefore important to predict such deformations and account for them in pre-operative planning. This paper examines two different, yet related methodologies for this task, both of which collect statistical information from a training set in order to construct a predictive model. The first one examines the modes of co-variation between shape and deformation, and is therefore purely shape-based. The second approach additionally incorporates knowledge about the biomechanical properties of anatomical structures in constructing a predictive model. The two methods are tested on simulated training sets. Preliminary results show average errors of 9% (both methods) for a simulated dataset that had a moderate statistical variation and 36% (first method) and 23% (second method) for a dataset with a large statistical variation. Use of the above methodologies will hopefully lead to better clinical outcome by improving pre-operative plans.
KW - Deformable mapping
KW - Finite element modeling and simulation
KW - Registration techniques
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U2 - 10.1007/978-3-540-40899-4_65
DO - 10.1007/978-3-540-40899-4_65
M3 - Conference contribution
AN - SCOPUS:84945570898
SN - 3540411895
SN - 9783540411895
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 634
EP - 642
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2000 - 3rd International Conference, Proceedings
A2 - Delp, Scott L.
A2 - DiGoia, Anthony M.
A2 - Jaramaz, Branislav
PB - Springer Verlag
Y2 - 11 October 2000 through 14 October 2000
ER -