A Fine-grained Orthodontics Segmentation Model for 3D Intraoral Scan Data

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 Fast-TGCN, which has been published in Computers in Biology and Medicine (https://www.sciencedirect.com/science/article/abs/pii/S0010482523012866)

Dataset

The 3D-IOSSeg dataset we proposed can be obtained at the following link: https://reurl.cc/0vjLXY

Usage

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

python train.py

Citation

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

Li, Juncheng, et al. "A fine-grained orthodontics segmentation model for 3D intraoral scan data." Computers in Biology and Medicine 168 (2024): 107821.

MIVRC / Fast-TGCN