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- W4302011191 abstract "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." @default.
- W4302011191 created "2022-10-06" @default.
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- W4302011191 date "2023-01-01" @default.
- W4302011191 modified "2023-10-18" @default.
- W4302011191 title "Bidirectional prediction of facial and bony shapes for orthognathic surgical planning" @default.
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- W4302011191 doi "https://doi.org/10.1016/j.media.2022.102644" @default.
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