Incremental kernel ridge regression for the prediction of soft tissue deformations

Binbin Pan; James J. Xia; Peng Yuan; Jaime Gateno; Horace H.S. Ip; Qizhen He; Philip K.M. Lee; Ben Chow; Xiaobo Zhou

doi:10.1007/978-3-642-33415-3_13

Incremental kernel ridge regression for the prediction of soft tissue deformations

Binbin Pan, James J. Xia, Peng Yuan, Jaime Gateno, Horace H.S. Ip, Qizhen He, Philip K.M. Lee, Ben Chow, Xiaobo Zhou

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

21 Scopus citations

Abstract

This paper proposes a nonlinear regression model to predict soft tissue deformation after maxillofacial surgery. The feature which served as input in the model is extracted with finite element model (FEM). The output in the model is the facial deformation calculated from the preoperative and postoperative 3D data. After finding the relevance between feature and facial deformation by using the regression model, we establish a general relationship which can be applied to all the patients. As a new patient comes, we predict his/her facial deformation by combining the general relationship and the new patient's biomechanical properties. Thus, our model is biomechanical relevant and statistical relevant. Validation on eleven patients demonstrates the effectiveness and efficiency of our method.

Original language	English (US)
Title of host publication	Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings
Editors	Nicholas Ayache, Herve Delingette, Polina Golland, Kensaku Mori
Publisher	Springer-Verlag
Pages	99-106
Number of pages	8
ISBN (Print)	9783642334146
DOIs	https://doi.org/10.1007/978-3-642-33415-3_13
State	Published - 2012
Event	15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012 - Nice, France Duration: Oct 1 2012 → Oct 5 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7510 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012
Country/Territory	France
City	Nice
Period	10/1/12 → 10/5/12

Keywords

Finite element model
Kernel ridge regression
Maxillofacial surgery
Soft tissue deformation

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-642-33415-3_13

Cite this

Pan, B., Xia, J. J., Yuan, P., Gateno, J., Ip, H. H. S., He, Q., Lee, P. K. M., Chow, B., & Zhou, X. (2012). Incremental kernel ridge regression for the prediction of soft tissue deformations. In N. Ayache, H. Delingette, P. Golland, & K. Mori (Eds.), Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings (pp. 99-106). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7510 LNCS). Springer-Verlag. https://doi.org/10.1007/978-3-642-33415-3_13

Incremental kernel ridge regression for the prediction of soft tissue deformations. / Pan, Binbin; Xia, James J.; Yuan, Peng et al.
Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings. ed. / Nicholas Ayache; Herve Delingette; Polina Golland; Kensaku Mori. Springer-Verlag, 2012. p. 99-106 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7510 LNCS).

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

Pan, B, Xia, JJ, Yuan, P, Gateno, J, Ip, HHS, He, Q, Lee, PKM, Chow, B & Zhou, X 2012, Incremental kernel ridge regression for the prediction of soft tissue deformations. in N Ayache, H Delingette, P Golland & K Mori (eds), Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7510 LNCS, Springer-Verlag, pp. 99-106, 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012, Nice, France, 10/1/12. https://doi.org/10.1007/978-3-642-33415-3_13

Pan B, Xia JJ, Yuan P, Gateno J, Ip HHS, He Q et al. Incremental kernel ridge regression for the prediction of soft tissue deformations. In Ayache N, Delingette H, Golland P, Mori K, editors, Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings. Springer-Verlag. 2012. p. 99-106. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-33415-3_13

Pan, Binbin ; Xia, James J. ; Yuan, Peng et al. / Incremental kernel ridge regression for the prediction of soft tissue deformations. Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings. editor / Nicholas Ayache ; Herve Delingette ; Polina Golland ; Kensaku Mori. Springer-Verlag, 2012. pp. 99-106 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "This paper proposes a nonlinear regression model to predict soft tissue deformation after maxillofacial surgery. The feature which served as input in the model is extracted with finite element model (FEM). The output in the model is the facial deformation calculated from the preoperative and postoperative 3D data. After finding the relevance between feature and facial deformation by using the regression model, we establish a general relationship which can be applied to all the patients. As a new patient comes, we predict his/her facial deformation by combining the general relationship and the new patient's biomechanical properties. Thus, our model is biomechanical relevant and statistical relevant. Validation on eleven patients demonstrates the effectiveness and efficiency of our method.",

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AU - Lee, Philip K.M.

AU - Chow, Ben

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N2 - This paper proposes a nonlinear regression model to predict soft tissue deformation after maxillofacial surgery. The feature which served as input in the model is extracted with finite element model (FEM). The output in the model is the facial deformation calculated from the preoperative and postoperative 3D data. After finding the relevance between feature and facial deformation by using the regression model, we establish a general relationship which can be applied to all the patients. As a new patient comes, we predict his/her facial deformation by combining the general relationship and the new patient's biomechanical properties. Thus, our model is biomechanical relevant and statistical relevant. Validation on eleven patients demonstrates the effectiveness and efficiency of our method.

AB - This paper proposes a nonlinear regression model to predict soft tissue deformation after maxillofacial surgery. The feature which served as input in the model is extracted with finite element model (FEM). The output in the model is the facial deformation calculated from the preoperative and postoperative 3D data. After finding the relevance between feature and facial deformation by using the regression model, we establish a general relationship which can be applied to all the patients. As a new patient comes, we predict his/her facial deformation by combining the general relationship and the new patient's biomechanical properties. Thus, our model is biomechanical relevant and statistical relevant. Validation on eleven patients demonstrates the effectiveness and efficiency of our method.

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Incremental kernel ridge regression for the prediction of soft tissue deformations

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