ZhangLingMing1 / TSGCNet

Two-Stream Graph Convolutional Network for Intra-Oral Scanner Image Segmentation

Prequisites

• python 3.7.4
• pytorch 1.4.0
• numpy 1.19.0
• plyfile 0.7.1

Introduction

This work is the pytorch implementation of TSGCN, which has been published in IEEE Transactions on Medical Imaging (https://ieeexplore.ieee.org/abstract/document/9594785/) and CVPR 2021 (https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_TSGCNet_Discriminative_Geometric_Feature_Learning_With_Two-Stream_Graph_Convolutional_Network_CVPR_2021_paper.html).

Usage

To train the TSGCN, please put the trainning data and testing data into data/train and data/test, respectively. Then, you can start to train a TSGCN model by following command.

python train.py

Citation

If you find our work useful in your research, please cite:

• Y. Zhao et al., "Two-Stream Graph Convolutional Network for Intra-Oral Scanner Image Segmentation," in IEEE Transactions on Medical Imaging, vol. 41, no. 4, pp. 826-835, April 2022, doi: 10.1109/TMI.2021.3124217.
• Zhang L, Zhao Y, Meng D, et al. TSGCNet: Discriminative Geometric Feature Learning with Two-Stream Graph Convolutional Network for 3D Dental Model Segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 6699-6708.