Simulation of Postoperative Facial Appearances via Geometric Deep Learning for Efficient Orthognathic Surgical Planning

Lei Ma, Deqiang Xiao, Daeseung Kim, Chunfeng Lian, Tianshu Kuang, Qin Liu, Hannah Deng, Erkun Yang, Michael A.K. Liebschner, Jaime Gateno, James J. Xia, Pew Thian Yap

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Orthognathic surgery corrects jaw deformities to improve aesthetics and functions. Due to the complexity of the craniomaxillofacial (CMF) anatomy, orthognathic surgery requires precise surgical planning, which involves predicting postoperative changes in facial appearance. To this end, most conventional methods involve simulation with biomechanical modeling methods, which are labor intensive and computationally expensive. Here we introduce a learning-based framework to speed up the simulation of postoperative facial appearances. Specifically, we introduce a facial shape change prediction network (FSC-Net) to learn the nonlinear mapping from bony shape changes to facial shape changes. FSC-Net is a point transform network weakly-supervised by paired preoperative and postoperative data without point-wise correspondence. In FSC-Net, a distance-guided shape loss places more emphasis on the jaw region. A local point constraint loss restricts point displacements to preserve the topology and smoothness of the surface mesh after point transformation. Evaluation results indicate that FSC-Net achieves 15× speedup with accuracy comparable to a state-of-the-art (SOTA) finite-element modeling (FEM) method.

Original languageEnglish (US)
Pages (from-to)336-345
Number of pages10
JournalIEEE Transactions on Medical Imaging
Issue number2
StatePublished - Feb 1 2023


  • 3D facial appearance
  • Orthognathic surgery
  • geometric deep learning
  • point transform network
  • topology preservation

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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