Deep Simulation of Facial Appearance Changes Following Craniomaxillofacial Bony Movements in Orthognathic Surgical Planning.
Deep Simulation of Facial Appearance Changes Following Craniomaxillofacial Bony Movements in Orthognathic Surgical Planning.
复制标题
颅颌面骨运动后面部外观变化的深度模拟在颌外科手术规划中的应用。
DOI：
10.1007/978-3-030-87202-1_44
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发表时间：
2021-09
期刊：
影响因子：
--
通讯作者：
Yap PT
中科院分区：
文献类型：
--
作者：
Ma L;Kim D;Lian C;Xiao D;Kuang T;Liu Q;Lang Y;Deng HH;Gateno J;Wu Y;Yang E;Liebschner MAK;Xia JJ;Yap PT
颅颌面畸形患者正颌外科手术中骨性节段的移动会导致面部形态的改变。用于模拟这种变化的常规生物力学方法（例如有限元建模（FEM））是劳动密集型的并且计算昂贵，这阻止了它们在临床环境中使用。为了克服这些限制，我们提出了一个深度学习框架来预测术后面部变化。具体而言，FC-Net，一个面部外观变化模拟网络，被开发用于预测与面部点云相关联的点位移向量。FC-Net从术前和模拟术后骨骼模型之间的骨骼运动向量中学习术前面部点云的点位移。FC-Net是一种弱监督点位移网络，使用具有严格点对点对应关系的成对数据进行训练。为了在点变换过程中保持人脸模型的拓扑结构，我们采用局部点变换损失来约束点的局部运动。对真实的患者数据的实验结果表明，所提出的框架可以预测手术后的面部外观变化显着快于一个国家的最先进的有限元方法具有可比的预测精度。
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.
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影响因子：
3.7
作者：
Knoops PGM;Borghi A;Ruggiero F;Badiali G;Bianchi A;Marchetti C;Rodriguez-Florez N;Breakey RWF;Jeelani O;Dunaway DJ;Schievano S
通讯作者：
Schievano S
影响因子：
3.9
作者：
Liang, Liang;Liu, Minliang;Sun, Wei
通讯作者：
Sun, Wei
影响因子：
10.9
作者：
Mendizabal, Andrea;Marquez-Neila, Pablo;Cotin, Stephane
通讯作者：
Cotin, Stephane
DOI：
10.1016/j.joms.2009.04.057
发表时间：
2009-10
期刊：
Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons
影响因子：
--
作者：
Xia JJ;Gateno J;Teichgraeber JF
通讯作者：
Teichgraeber JF
影响因子：
6.2
作者：
Yin, Kangxue;Huang, Hui;Zhang, Hao
通讯作者：
Zhang, Hao