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Craniomaxillofacial Deformity Correction via Sparse Representation in Coherent Space

  • Zuoyong Li
  • Le An
  • Jun Zhang
  • Li Wang
  • James J. Xia
  • Dinggang ShenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9352)

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.

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© Springer International Publishing Switzerland 2015

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Authors and Affiliations

  • Zuoyong Li
    • 1
    • 2
  • Le An
    • 2
  • Jun Zhang
    • 2
  • Li Wang
    • 2
  • James J. Xia
    • 3
    • 4
  • Dinggang Shen
    • 2
    Email author
  1. 1.Department of Computer ScienceMinjiang UniversityFuzhouChina
  2. 2.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA
  3. 3.Department of Oral and Maxillofacial SurgeryHouston Methodist Research Institute, Weill Medical College of Cornell UniversityNew YorkUSA
  4. 4.Department of Oral and Craniomaxillofacial Science, Shanghai Ninth Hospital,Shanghai Jiao Tong University, School of MedicineShanghaiChina

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