An integration of statistical deformable model and finite element method for bone-related soft tissue prediction in orthognathic surgery planning

Qizhen He; Jun Feng; Horace H S Ip; James Xia; Xianbin Cao

doi:10.1007/978-3-540-79982-5_4

An integration of statistical deformable model and finite element method for bone-related soft tissue prediction in orthognathic surgery planning

Qizhen He, Jun Feng, Horace H S Ip, James Xia, Xianbin Cao

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

4 Scopus citations

Abstract

In this paper, we propose a novel statistical deformable model for bone-related soft-tissue prediction, which we called Br-SDM. In Br-SDM, we have integrated Finite Element Model(FEM) and Statistical Deformable Model(SDM) to achieve both accurate and efficient prediction for orthognathic surgery planning. By combining FEM-based surgery simulation for sample generation and SDM for soft tissue prediction, we are able to capture the prior knowledge of bone-related soft-tissue deformation for different surgical plans. Then the post-operative appearance can be predicted in a more efficient way from a Br-SDM based optimization. Our experiments have shown that Br-SDM is able to give comparable soft-tissue prediction accuracy with respect to conventional FEM-based prediction while only requires 10% of its computational cost.

Original language	English (US)
Title of host publication	Medical Imaging and Augmented Reality - 4th International Workshop, Proceedings
Pages	31-39
Number of pages	9
DOIs	https://doi.org/10.1007/978-3-540-79982-5_4
State	Published - 2008
Event	4th International Workshop on Medical Imaging and Augmented Reality, MIAR 2008 - Tokyo, Japan Duration: Aug 1 2008 → Aug 2 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5128 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	4th International Workshop on Medical Imaging and Augmented Reality, MIAR 2008
Country/Territory	Japan
City	Tokyo
Period	8/1/08 → 8/2/08

Keywords

Finite Element Method
Operation Prediction
Orthognathic Surgery
Statistical Deformable Model
Surgery Planning

ASJC Scopus subject areas

Computer Science(all)
Biochemistry, Genetics and Molecular Biology(all)
Theoretical Computer Science

Access to Document

10.1007/978-3-540-79982-5_4

Cite this

He, Q., Feng, J., Ip, H. H. S., Xia, J., & Cao, X. (2008). An integration of statistical deformable model and finite element method for bone-related soft tissue prediction in orthognathic surgery planning. In Medical Imaging and Augmented Reality - 4th International Workshop, Proceedings (pp. 31-39). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5128 LNCS). https://doi.org/10.1007/978-3-540-79982-5_4

An integration of statistical deformable model and finite element method for bone-related soft tissue prediction in orthognathic surgery planning. / He, Qizhen; Feng, Jun; Ip, Horace H S et al.
Medical Imaging and Augmented Reality - 4th International Workshop, Proceedings. 2008. p. 31-39 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5128 LNCS).

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

He, Q, Feng, J, Ip, HHS, Xia, J & Cao, X 2008, An integration of statistical deformable model and finite element method for bone-related soft tissue prediction in orthognathic surgery planning. in Medical Imaging and Augmented Reality - 4th International Workshop, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5128 LNCS, pp. 31-39, 4th International Workshop on Medical Imaging and Augmented Reality, MIAR 2008, Tokyo, Japan, 8/1/08. https://doi.org/10.1007/978-3-540-79982-5_4

He Q, Feng J, Ip HHS, Xia J, Cao X. An integration of statistical deformable model and finite element method for bone-related soft tissue prediction in orthognathic surgery planning. In Medical Imaging and Augmented Reality - 4th International Workshop, Proceedings. 2008. p. 31-39. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-79982-5_4

He, Qizhen ; Feng, Jun ; Ip, Horace H S et al. / An integration of statistical deformable model and finite element method for bone-related soft tissue prediction in orthognathic surgery planning. Medical Imaging and Augmented Reality - 4th International Workshop, Proceedings. 2008. pp. 31-39 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In this paper, we propose a novel statistical deformable model for bone-related soft-tissue prediction, which we called Br-SDM. In Br-SDM, we have integrated Finite Element Model(FEM) and Statistical Deformable Model(SDM) to achieve both accurate and efficient prediction for orthognathic surgery planning. By combining FEM-based surgery simulation for sample generation and SDM for soft tissue prediction, we are able to capture the prior knowledge of bone-related soft-tissue deformation for different surgical plans. Then the post-operative appearance can be predicted in a more efficient way from a Br-SDM based optimization. Our experiments have shown that Br-SDM is able to give comparable soft-tissue prediction accuracy with respect to conventional FEM-based prediction while only requires 10% of its computational cost.",

keywords = "Finite Element Method, Operation Prediction, Orthognathic Surgery, Statistical Deformable Model, Surgery Planning",

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AB - In this paper, we propose a novel statistical deformable model for bone-related soft-tissue prediction, which we called Br-SDM. In Br-SDM, we have integrated Finite Element Model(FEM) and Statistical Deformable Model(SDM) to achieve both accurate and efficient prediction for orthognathic surgery planning. By combining FEM-based surgery simulation for sample generation and SDM for soft tissue prediction, we are able to capture the prior knowledge of bone-related soft-tissue deformation for different surgical plans. Then the post-operative appearance can be predicted in a more efficient way from a Br-SDM based optimization. Our experiments have shown that Br-SDM is able to give comparable soft-tissue prediction accuracy with respect to conventional FEM-based prediction while only requires 10% of its computational cost.

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An integration of statistical deformable model and finite element method for bone-related soft tissue prediction in orthognathic surgery planning

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