Virtual reconstruction of midfacial bone defect based on generative adversarial network.

ABSTRACT:

Background

The study aims to evaluate the accuracy of the generative adversarial networks (GAN) for reconstructing bony midfacial defects.

Methods

According to anatomy, the bony midface was divided into five subunit structural regions and artificial defects are manually created on the corresponding CT images. GAN is trained to reconstruct artificial defects to their previous normal shape and tested. The clinical defects are reconstructed by the trained GAN, where the midspan defects were used for qualitative evaluation and the unilateral defects were used for quantitative evaluation. The cosine similarity and the mean error are used to evaluate the accuracy of reconstruction. The Mann-Whitney U test is used to detect whether reconstruction errors were consistent in artificia

SUBMITTER: Xiong YT 

PROVIDER: S-EPMC9235085 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature