• March. 23th, 2024: Our paper was accepted by IEEE Journal of Biomedical and Health Informatics (JBHI), congratulations!🎉🎉🎉🎉
  
• April. 8th, 2024: We released the NKUT dataset. Now, researchers can apply to obtain the dataset.🎉🎉🎉🎉
  
• May. 15th, 2024: We released the 2D and 3D WTNet model. 🎉🎉🎉🎉
  
• Dec. 26th, 2024: We released the training codes. 🎉🎉🎉🎉
  happy new year!

• NKUT Dataset release
• WTNet 2D model code release
• WTNet 3D model code release
• Training code release

• 1. Download and fill in the Application.pdf PDF file in the repository. Please note that all items in the file need to be filled in completely and cannot be left blank, otherwise it may affect the acquisition of the dataset.
• 2. Send an email to aics@nankai.edu.cn and copy to zzh_nkcs@mail.nankai.edu.cn. The subject of the email should be "NKUT Dataset Request" and briefly describe your name, contact information and institution or organization in the content of the email. Remember to upload the PDF completed in last step as an attachment of your email.
• 3. We will review your application and notify you via email whether your application has been approved or if further submission of materials is required within two weeks. Please arrange your time reasonably.
• 4. For researchers who pass the application, we will attach a link to obtain the dataset with the email. You will get about 30 cases of NKUT dataset and their corresponding pixel-level expert annotations, a doc file recording the details of each data will also be included.

The 2D WTNet model is in ./networks/WTNet/WTNet.py

The 3D WTNet model is in ./modles/WTNet.py

Adjust the parameters in the final part of train_WTNet.py according to your situation and run train_WTNet.py.

If you used NKUT in your own research, please give us a star and cite our paper below:

@ARTICLE{10485282,
  author={Zhou, Zhenhuan and Chen, Yuzhu and He, Along and Que, Xitao and Wang, Kai and Yao, Rui and Li, Tao},
  journal={IEEE Journal of Biomedical and Health Informatics}, 
  title={NKUT: Dataset and Benchmark for Pediatric Mandibular Wisdom Teeth Segmentation}, 
  year={2024},
  volume={28},
  number={6},
  pages={3523-3533},
  keywords={Teeth;Dentistry;Image segmentation;Task analysis;Bones;Annotations;Three-dimensional displays;CBCT dataset;pediatric wisdom teeth segmentation;pediatric germectomy;multi-scale feature fusion},
  doi={10.1109/JBHI.2024.3383222}}

Code can only be used for ACADEMIC PURPOSES. NO COMERCIAL USE is allowed. Copyright © College of Computer Science, Nankai University. All rights reserved.