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

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

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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 patient's 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)
Article number9336225
Pages (from-to)2958-2966
Number of pages9
JournalIEEE Journal of Biomedical and Health Informatics
VolumePP
Issue number8
Early online dateJan 26 2021
DOIs
StateE-pub ahead of print - Jan 26 2021

Keywords

  • 3D point cloud
  • Dentofacial deformity
  • geometric deep learning
  • orthognathic surgical planning
  • shape estimation

ASJC Scopus subject areas

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

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