Craniomaxillofacial deformity correction via sparse representation in coherent space

Zuoyong Li, Le An, Jun Zhang, Li Wang, James J. Xia, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Orthognathic surgery is popular for patients with craniomaxillofacial (CMF) deformity. For orthognathic surgical planning, it is critical to have a patient-specific jaw reference model as guidance. One way is to estimate a normal jaw shape for the patient, by first searching for a normal subject with similar midface and then borrowing his/her (normal) jaw shape as reference. Intuitively, we can search for multiple normal subjects with similar midface and then linearly combine them as final reference. The respective coefficients for linear combination can be estimated, i.e., by sparse representation of patient’s midface by midfaces of all training normal subjects. However, this approach implicitly assumes that the representation of midface shapes is strongly correlated with the representation of jaw shapes, which is unfortunately difficult to meet in practice due to generally different data distributions of shapes of midfaces and jaws. To address this limitation, we propose to estimate the patient-specific jaw reference model in a coherent space. Specifically, we first employ canonical correlation analysis (CCA) to map the midface and jaw landmarks of training normal subjects into a coherent space, in which their correlation is maximized. Then, in the coherent space, the mapped midface landmarks of patient can be sparsely represented by the mapped midface landmarks of training normal subjects. Those learned sparse coefficients can now be used to combine the jaw landmarks of training normal subjects for estimating the normal jaw landmarks for patient and then building normal jaw shape reference model. Moreover, we also iteratively maximize the correlation between the midface and the jaw shapes in the new coherent space with a multi-layer mapping and refinement (MMR) process. Experimental results on real clinical data show that the proposed method can more accurately reconstruct the normal jaw shape for patient than the competing methods.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages69-76
Number of pages8
Volume9352
ISBN (Print)9783319248875
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015 - Munich, Germany
Duration: 2015 Oct 52015 Oct 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9352
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015
CountryGermany
CityMunich
Period15/10/515/10/5

Fingerprint

Sparse Representation
Landmarks
Reference Model
Surgery
Planning
Canonical Correlation Analysis
Data Distribution
Coefficient
Estimate
Guidance
Multilayer
Linear Combination
Refinement
Linearly
Maximise
Training
Experimental Results

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Li, Z., An, L., Zhang, J., Wang, L., Xia, J. J., & Shen, D. (2015). Craniomaxillofacial deformity correction via sparse representation in coherent space. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9352, pp. 69-76). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9352). Springer Verlag. https://doi.org/10.1007/978-3-319-24888-2_9

Craniomaxillofacial deformity correction via sparse representation in coherent space. / Li, Zuoyong; An, Le; Zhang, Jun; Wang, Li; Xia, James J.; Shen, Dinggang.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9352 Springer Verlag, 2015. p. 69-76 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9352).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Li, Z, An, L, Zhang, J, Wang, L, Xia, JJ & Shen, D 2015, Craniomaxillofacial deformity correction via sparse representation in coherent space. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 9352, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9352, Springer Verlag, pp. 69-76, 6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015, Munich, Germany, 15/10/5. https://doi.org/10.1007/978-3-319-24888-2_9
Li Z, An L, Zhang J, Wang L, Xia JJ, Shen D. Craniomaxillofacial deformity correction via sparse representation in coherent space. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9352. Springer Verlag. 2015. p. 69-76. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-24888-2_9
Li, Zuoyong ; An, Le ; Zhang, Jun ; Wang, Li ; Xia, James J. ; Shen, Dinggang. / Craniomaxillofacial deformity correction via sparse representation in coherent space. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9352 Springer Verlag, 2015. pp. 69-76 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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