Abstract
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.
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© 2000 Springer-Verlag Berlin Heidelberg
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Kyriacou, S.K., Shen, D., Davatzikos, C. (2000). A Framework for Predictive Modeling of Intra-operative Deformations: A Simulation-Based Study. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2000. MICCAI 2000. Lecture Notes in Computer Science, vol 1935. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-40899-4_65
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DOI: https://doi.org/10.1007/978-3-540-40899-4_65
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