计算机科学
人工智能
点云
转化(遗传学)
多边形网格
点(几何)
推论
面子(社会学概念)
正颌外科
计算机视觉
手术计划
卷积(计算机科学)
数学
人工神经网络
口腔正畸科
医学
基因
计算机图形学(图像)
放射科
生物化学
社会学
化学
社会科学
几何学
作者
Lei Ma,Chunfeng Lian,Daeseung Kim,Deqiang Xiao,Dongming Wei,Qin Liu,Tianshu Kuang,Maryam Ghanbari,Guoshi Li,Jaime Gateño,Steve Guofang Shen,Li Wang,Dinggang Shen,James J. Xia,Pew‐Thian Yap
标识
DOI:10.1016/j.media.2022.102644
摘要
This paper proposes a deep learning framework to encode subject-specific transformations between facial and bony shapes for orthognathic surgical planning. Our framework involves a bidirectional point-to-point convolutional network (P2P-Conv) to predict the transformations between facial and bony shapes. P2P-Conv is an extension of the state-of-the-art P2P-Net and leverages dynamic point-wise convolution (i.e., PointConv) to capture local-to-global spatial information. Data augmentation is carried out in the training of P2P-Conv with multiple point subsets from the facial and bony shapes. During inference, network outputs generated for multiple point subsets are combined into a dense transformation. Finally, non-rigid registration using the coherent point drift (CPD) algorithm is applied to generate surface meshes based on the predicted point sets. Experimental results on real-subject data demonstrate that our method substantially improves the prediction of facial and bony shapes over state-of-the-art methods.
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