Deep Simulation of Facial Appearance Changes Following Craniomaxillofacial Bony Movements in Orthognathic Surgical Planning

Authors

Lei Ma
Daeseung Kim
Chunfeng Lian
Deqiang Xiao
Tianshu Kuang
Qin Liu
Yankun Lang
Hannah H Deng
Jaime Gateno
Ye Wu
Erkun Yang
Michael A K Liebschner
James J Xia
Pew-Thian Yap

Publication Date

1-1-2021

Journal

Medical Image Computing and Computer Assisted Intervention

DOI

10.1007/978-3-030-87202-1_44

PMID

34966912

PMCID

PMC8713535

PubMedCentral® Posted Date

12-28-2021

PubMedCentral® Full Text Version

Author MSS

Published Open-Access

yes

Keywords

Facial appearance change, Point transform network, Topology preservation

Abstract

Facial appearance changes with the movements of bony segments in orthognathic surgery of patients with craniomaxillofacial (CMF) deformities. Conventional bio-mechanical methods, such as finite element modeling (FEM), for simulating such changes, are labor intensive and computationally expensive, preventing them from being used in clinical settings. To overcome these limitations, we propose a deep learning framework to predict post-operative facial changes. Specifically, FC-Net, a facial appearance change simulation network, is developed to predict the point displacement vectors associated with a facial point cloud. FC-Net learns the point displacements of a pre-operative facial point cloud from the bony movement vectors between pre-operative and simulated post-operative bony models. FC-Net is a weakly-supervised point displacement network trained using paired data with strict point-to-point correspondence. To preserve the topology of the facial model during point transform, we employ a local-point-transform loss to constrain the local movements of points. Experimental results on real patient data reveal that the proposed framework can predict post-operative facial appearance changes remarkably faster than a state-of-the-art FEM method with comparable prediction accuracy.