@inproceedings{b63bb6286a3b4cc29b2057c00fadb775,
title = "Deep Learning-Based Facial Appearance Simulation Driven by Surgically Planned Craniomaxillofacial Bony Movement",
abstract = "Simulating facial appearance change following bony movement is a critical step in orthognathic surgical planning for patients with jaw deformities. Conventional biomechanics-based methods such as the finite-element method (FEM) are labor intensive and computationally inefficient. Deep learning-based approaches can be promising alternatives due to their high computational efficiency and strong modeling capability. However, the existing deep learning-based method ignores the physical correspondence between facial soft tissue and bony segments and thus is significantly less accurate compared to FEM. In this work, we propose an Attentive Correspondence assisted Movement Transformation network (ACMT-Net) to estimate the facial appearance by transforming the bony movement to facial soft tissue through a point-to-point attentive correspondence matrix. Experimental results on patients with jaw deformity show that our proposed method can achieve comparable facial change prediction accuracy compared with the state-of-the-art FEM-based approach with significantly improved computational efficiency.",
keywords = "Correspondence assisted movement transformation, Cross point-set attention, Deep learning, Simulation, Surgical planning",
author = "Xi Fang and Daeseung Kim and Xuanang Xu and Tianshu Kuang and Deng, {Hannah H.} and Barber, {Joshua C.} and Nathan Lampen and Jaime Gateno and Liebschner, {Michael A.K.} and Xia, {James J.} and Pingkun Yan",
note = "Funding Information: Acknowledgements. This work was partially supported by NIH under awards R01 DE022676, R01 DE027251 and R01 DE021863. Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 ; Conference date: 18-09-2022 Through 22-09-2022",
year = "2022",
doi = "10.1007/978-3-031-16449-1_54",
language = "English (US)",
isbn = "9783031164484",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "565--574",
editor = "Linwei Wang and Qi Dou and Fletcher, {P. Thomas} and Stefanie Speidel and Shuo Li",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings",
address = "Germany",
}