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Med. Image Anal. , Jan 26 2024, 93 S1361-8415(24)00019-7 | https://doi.org/10.1016/j.media.2024.103094
Correspondence attention for facial appearance simulation
Xi Fang a,1, Daeseung Kim b,1, Xuanang Xu a, Tianshu Kuang b, Nathan Lampen a, Jungwook Lee a, Hannah H. Deng b, Michael A.K. Liebschner c, James J. Xia b,d, Jaime Gateno b,d, Pingkun Yan a
aDepartment of Biomedical Engineering and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
bDepartment of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX 77030, USA
cDepartment of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA
dWeill Medical College, Cornell University, New York, NY, 10021, USA
1Equally contributed first authors.
Correspondence : Jaime Gateno, Pingkun Yan
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.
논문정보
- Research article
- 2024년 01월 (BRIC 등록일 2024-02-06)
- 국외 연구진
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