Estimating anatomically-correct reference model for craniomaxillofacial deformity via sparse representation

Yi Ren, Li Wang, Yaozong Gaol, Zhen Tang, Ken C hung Chen, Jianfu Li, Steve G F Shen, Yan Jin Yan, Philip K M Lee, Ben Chow, James J. Xia, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Citations (Scopus)

Abstract

The success of craniomaxillofacial (CMF) surgery depends not only on the surgical techniques, but also upon an accurate surgical planning. However, surgical planning for CMF surgery is challenging due to the absence of a patient-specific reference model. In this paper, we present a method to automatically estimate an anatomically correct reference shape of jaws for the patient requiring orthognathic surgery, a common type of CMF surgery. We employ the sparse representation technique to represent the normal regions of the patient with respect to the normal subjects. The estimated representation is then used to reconstruct a patient-specific reference model with "restored" normal anatomy of the jaws. We validate our method on both synthetic subjects and patients. Experimental results show that our method can effectively reconstruct the normal shape of jaw for patients. Also, a new quantitative measurement is introduced to quantify the CMF deformity and validate the method in a quantitative approach, which is rarely used before.

Original languageEnglish
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages73-80
Number of pages8
Volume17
Publication statusPublished - 2014

ASJC Scopus subject areas

  • Medicine(all)

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    Ren, Y., Wang, L., Gaol, Y., Tang, Z., Chen, K. C. H., Li, J., Shen, S. G. F., Jin Yan, Y., Lee, P. K. M., Chow, B., Xia, J. J., & Shen, D. (2014). Estimating anatomically-correct reference model for craniomaxillofacial deformity via sparse representation. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Vol. 17, pp. 73-80)