

@article{wang2023root,
  title={Root canal treatment planning by automatic tooth and root canal segmentation in dental CBCT with deep multi-task feature learning},
  author={Wang, Yiwei and Xia, Wenjun and Yan, Zhennan and Zhao, Liang and Bian, Xiaohe and Liu, Chang and Qi, Zhengnan and Zhang, Shaoting and Tang, Zisheng},
  journal={Medical image analysis},
  volume={85},
  pages={102750},
  year={2023},
  publisher={Elsevier}
}

@article{dot2024dentalsegmentator,
  title={DentalSegmentator: robust open source deep learning-based CT and CBCT image segmentation},
  author={Dot, Gauthier and Chaurasia, Akhilanand and Dubois, Guillaume and Savoldelli, Charles and Haghighat, Sara and Azimian, Sarina and Taramsari, Ali Rahbar and Sivaramakrishnan, Gowri and Issa, Julien and Dubey, Abhishek and others},
  journal={Journal of dentistry},
  volume={147},
  pages={105130},
  year={2024},
  publisher={Elsevier}
}


@article{cui2022fully,
  title={A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images},
  author={Cui, Zhiming and Fang, Yu and Mei, Lanzhuju and Zhang, Bojun and Yu, Bo and Liu, Jiameng and Jiang, Caiwen and Sun, Yuhang and Ma, Lei and Huang, Jiawei and others},
  journal={Nature communications},
  volume={13},
  number={1},
  pages={2096},
  year={2022},
  publisher={Nature Publishing Group UK London}
}

@article{huang2025maxillofacial,
  title={Maxillofacial bone movements-aware dual graph convolution approach for postoperative facial appearance prediction},
  author={Huang, Xinrui and He, Dongming and Li, Zhenming and Zhang, Xiaofan and Wang, Xudong},
  journal={Medical Image Analysis},
  volume={99},
  pages={103350},
  year={2025},
  publisher={Elsevier}
}

@inproceedings{huang2024iossam,
  title={IOSSAM: Label Efficient Multi-view Prompt-Driven Tooth Segmentation},
  author={Huang, Xinrui and He, Dongming and Li, Zhenming and Zhang, Xiaofan and Wang, Xudong},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={632--642},
  year={2024},
  organization={Springer}
}

@article{isensee2021nnu,
  title={nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation},
  author={Isensee, Fabian and Jaeger, Paul F and Kohl, Simon AA and Petersen, Jens and Maier-Hein, Klaus H},
  journal={Nature methods},
  volume={18},
  number={2},
  pages={203--211},
  year={2021},
  publisher={Nature Publishing Group}
}

@article{chen2021transunet,
  title={Transunet: Transformers make strong encoders for medical image segmentation},
  author={Chen, Jieneng and Lu, Yongyi and Yu, Qihang and Luo, Xiangde and Adeli, Ehsan and Wang, Yan and Lu, Le and Yuille, Alan L and Zhou, Yuyin},
  journal={arXiv preprint arXiv:2102.04306},
  year={2021}
}

@article{zhou2023nnformer,
  title={nnformer: Volumetric medical image segmentation via a 3d transformer},
  author={Zhou, Hong-Yu and Guo, Jiansen and Zhang, Yinghao and Han, Xiaoguang and Yu, Lequan and Wang, Liansheng and Yu, Yizhou},
  journal={IEEE transactions on image processing},
  volume={32},
  pages={4036--4045},
  year={2023},
  publisher={IEEE}
}

@article{shaker2024unetr++,
  title={UNETR++: delving into efficient and accurate 3D medical image segmentation},
  author={Shaker, Abdelrahman and Maaz, Muhammad and Rasheed, Hanoona and Khan, Salman and Yang, Ming-Hsuan and Khan, Fahad Shahbaz},
  journal={IEEE Transactions on Medical Imaging},
  volume={43},
  number={9},
  pages={3377--3390},
  year={2024},
  publisher={IEEE}
}

@article{ma2024u,
  title={U-mamba: Enhancing long-range dependency for biomedical image segmentation},
  author={Ma, Jun and Li, Feifei and Wang, Bo},
  journal={arXiv preprint arXiv:2401.04722},
  year={2024}
}

@article{liu2024vmamba,
  title={Vmamba: Visual state space model},
  author={Liu, Yue and Tian, Yunjie and Zhao, Yuzhong and Yu, Hongtian and Xie, Lingxi and Wang, Yaowei and Ye, Qixiang and Jiao, Jianbin and Liu, Yunfan},
  journal={Advances in neural information processing systems},
  volume={37},
  pages={103031--103063},
  year={2024}
}

@inproceedings{liu2024swin,
  title={Swin-umamba: Mamba-based unet with imagenet-based pretraining},
  author={Liu, Jiarun and Yang, Hao and Zhou, Hong-Yu and Xi, Yan and Yu, Lequan and Li, Cheng and Liang, Yong and Shi, Guangming and Yu, Yizhou and Zhang, Shaoting and others},
  booktitle={International conference on medical image computing and computer-assisted intervention},
  pages={615--625},
  year={2024},
  organization={Springer}
}

@article{elgarba2023deep,
  title={Deep learning-based segmentation of dental implants on cone-beam computed tomography images: A validation study},
  author={Elgarba, Bahaaeldeen M and Van Aelst, Stijn and Swaity, Abdullah and Morgan, Nermin and Shujaat, Sohaib and Jacobs, Reinhilde},
  journal={Journal of Dentistry},
  volume={137},
  pages={104639},
  year={2023},
  publisher={Elsevier}
}

@article{he2024vista3d,
  title={Vista3d: Versatile imaging segmentation and annotation model for 3d computed tomography},
  author={He, Yufan and Guo, Pengfei and Tang, Yucheng and Myronenko, Andriy and Nath, Vishwesh and Xu, Ziyue and Yang, Dong and Zhao, Can and Simon, Benjamin and Belue, Mason and others},
  journal={CoRR},
  year={2024}
}

@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Yanfeng and Li, Lingrui and Lu, Le and Xu, Minfeng},
    title     = {nnWNet: Rethinking the Use of Transformers in Biomedical Image Segmentation and Calling for a Unified Evaluation Benchmark},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20852-20862}
}

@inproceedings{bolelli2025segmenting, link={}, isbn={}, source_code={}, doi={}, note={}, publisher={IEEE}, venue={Nashville, Tennessee, USA}, month={Mar}, year={2025}, pages={1--10}, booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, title={{Segmenting Maxillofacial Structures in CBCT Volume}}, author={Bolelli, Federico and Marchesini, Kevin and van Nistelrooij, Niels and Lumetti, Luca and Pipoli, Vittorio and Ficarra, Elisa and Vinayahalingam, Shankeeth and Grana, Costantino}}

@ARTICLE{bolelli2024segmenting,   author={Bolelli, Federico and Lumetti, Luca and Vinayahalingam, Shankeeth and Di Bartolomeo, Mattia and Pellacani, Arrigo and Marchesini, Kevin and van Nistelrooij, Niels and van Lierop, Pieter and Xi, Tong and Liu, Yusheng and Xin, Rui and Yang, Tao and Wang, Lisheng and Wang, Haoshen and Xu, Chenfan and Cui, Zhiming and Wodzinski, Marek and Müller, Henning and Kirchhoff, Yannick and R. Rokuss, Maximilian and Maier-Hein, Klaus and Han, Jaehwan and Kim, Wan and Ahn, Hong-Gi and Szczepański, Tomasz and Grzeszczyk, Michal K. and Korzeniowski, Przemyslaw and Caselles Ballester, Vicent amd Paolo Burgos-Artizzu, Xavier and Prados Carrasco, Ferran and Berge’, Stefaan and van Ginneken, Bram and Anesi, Alexandre and Grana, Costantino},   title={{Segmenting the Inferior Alveolar Canal in CBCTs Volumes: the ToothFairy Challenge}},   journal={IEEE Transactions on Medical Imaging},   pages={1--17},   year=2024,   month={Dec},   publisher={IEEE},   doi={https://doi.org/10.1109/TMI.2024.3523096},   source_code={https://github.com/AImageLab-zip/ToothFairy},   issn={1558-254X} }

@article{bolelli2024enhancing, 	issn={2169-3536}, 	isbn={}, 	doi={https://doi.org/10.1109/ACCESS.2024.3408629}, 	publisher={IEEE}, 	year={2024}, 	pages={1--12}, 	journal={IEEE Access}, 	title={{Enhancing Patch-Based Learning for the Segmentation of the Mandibular Canal}}, 	author={Lumetti, Luca and Pipoli, Vittorio and Bolelli, Federico and Ficarra, Elisa and Grana, Costantino}, }

@article{zhang2020kdecay,
  title={kDecay: Just adding k-decay items on Learning-Rate Schedule to improve Neural Networks},
  author={Zhang, Tao and Li, Wei},
  journal={arXiv preprint arXiv:2004.05909},
  year={2020}
}