Estimating Reference Bony Shape Models for Orthognathic Surgical Planning Using 3D Point-Cloud Deep Learning

Deqiang Xiao, Chunfeng Lian, Han Deng, Tianshu Kuang, Qin Liu, Lei Ma, Daeseung Kim, Yankun Lang, Xu Chen, Jaime Gateno, Steve Shen, James Jiong Xia, Pew Thian Yap

Research output: Contribution to journalArticle

Abstract

Orthognathic surgical outcomes rely heavily on the quality of surgical planning. Automatic estimation of a reference facial bone shape significantly reduces experience-dependent variability and improves planning accuracy and efficiency. We propose an end-to-end deep learning framework to estimate patient-specific reference bony shape models for patients with orthognathic deformities. Specifically, we apply a point-cloud network to learn a vertex-wise deformation field from a patients deformed bony shape, represented as a point cloud. The estimated deformation field is then used to correct the deformed bony shape to output a patient-specific reference bony surface model. To train our network effectively, we introduce a simulation strategy to synthesize deformed bones from any given normal bone, producing a relatively large and diverse dataset of shapes for training. Our method was evaluated using both synthetic and real patient data. Experimental results show that our framework estimates realistic reference bony shape models for patients with varying deformities. The performance of our method is consistently better than an existing method and several deep point-cloud networks. Our end-to-end estimation framework based on geometric deep learning shows great potential for improving clinical workflows.

Original languageEnglish (US)
JournalIEEE Journal of Biomedical and Health Informatics
VolumePP
Early online dateJan 26 2021
DOIs
StateE-pub ahead of print - Jan 26 2021

Keywords

  • 3D point cloud
  • Bones
  • Convolution
  • Deformable models
  • Dentofacial deformity
  • Faces
  • geometric deep learning
  • orthognathic surgical planning
  • Shape
  • shape estimation
  • Surgery
  • Three-dimensional displays

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

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