Language
English
Publication Date
4-1-2024
Journal
Medical Image Analysis
DOI
10.1016/j.media.2024.103094
PMID
38306802
PMCID
PMC11265218
PubMedCentral® Posted Date
4-1-2025
PubMedCentral® Full Text Version
Author MSS
Abstract
In orthognathic surgical planning for patients with jaw deformities, it is crucial to accurately simulate the changes in facial appearance that follow the bony movement. Compared with the traditional biomechanics-based methods like the finite-element method (FEM), which are both labor-intensive and computationally inefficient, deep learning-based methods offer an efficient and robust modeling alternative. However, current methods do not account for the physical relationship between facial soft tissue and bony structure, causing them to fall short in accuracy compared to FEM. In this work, we propose an Attentive Correspondence assisted Movement Transformation network (ACMT-Net) to predict facial changes by correlating facial soft tissue changes with bony movement through a point-to-point attentive correspondence matrix. To ensure efficient training, we also introduce a contrastive loss for self-supervised pre-training of the ACMT-Net with a k-Nearest Neighbors (k-NN) based clustering. Experimental results on patients with jaw deformities show that our proposed solution can achieve significantly improved computational efficiency over the state-of-the-art FEM-based method with comparable facial change prediction accuracy.
Keywords
Humans, Face, Biomechanical Phenomena, Computer Simulation, Movement, Surgical planning, Facial simulation, Image-guided surgery, Attentive correspondence
Published Open-Access
yes
Recommended Citation
Fang, Xi; Kim, Daeseung; Xu, Xuanang; et al., "Correspondence Attention for Facial Appearance Simulation" (2024). Faculty and Staff Publications. 4376.
https://digitalcommons.library.tmc.edu/baylor_docs/4376