@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}
}

@inproceedings{isensee2024nnu,
  title={nnu-net revisited: A call for rigorous validation in 3d medical image segmentation},
  author={Isensee, Fabian and Wald, Tassilo and Ulrich, Constantin and Baumgartner, Michael and Roy, Saikat and Maier-Hein, Klaus and Jaeger, Paul F},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={488--498},
  year={2024},
  organization={Springer}
}

@misc{ronneberger2015unet,
      title={U-Net: Convolutional Networks for Biomedical Image Segmentation}, 
      author={Olaf Ronneberger and Philipp Fischer and Thomas Brox},
      year={2015},
      eprint={1505.04597},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1505.04597}, 
}

@inproceedings{cciccek20163dunet,
  title={3D U-Net: learning dense volumetric segmentation from sparse annotation},
  author={{\c{C}}i{\c{c}}ek, {\"O}zg{\"u}n and Abdulkadir, Ahmed and Lienkamp, Soeren S and Brox, Thomas and Ronneberger, Olaf},
  booktitle={Medical Image Computing and Computer-Assisted Intervention--MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19},
  pages={424--432},
  year={2016},
  organization={Springer}
}

% related work

@misc{huang2025H3DE,
      title={H3DE-Net: Efficient and Accurate 3D Landmark Detection in Medical Imaging}, 
      author={Zhen Huang and Ronghao Xu and Xiaoqian Zhou and Yangbo Wei and Suhua Wang and Xiaoxin Sun and Han Li and Qingsong Yao},
      year={2025},
      eprint={2502.14221},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2502.14221}, 
}

@article{zhang2024SRLD,
  title={Super-resolution landmark detection networks for medical images},
  author={Zhang, Runshi and Mo, Hao and Hu, Weini and Jie, Bimeng and Xu, Lin and He, Yang and Ke, Jia and Wang, Junchen},
  journal={Computers in Biology and Medicine},
  volume={182},
  pages={109095},
  year={2024},
  publisher={Elsevier}
}

@article{Jonkers2025landmarker,
    title = {landmarker: A Toolkit for Anatomical Landmark Localization in 2D/3D Images},
    journal = {SoftwareX},
    volume = {30},
    pages = {102165},
    year = {2025},
    issn = {2352-7110},
    doi = {10.1016/j.softx.2025.102165},
    author = {Jonkers, Jef and Duchateau, Luc and Van Wallendael, Glenn and Van Hoecke, Sofie}
}

@misc{Jonkers2025uq,
    author = {Jonkers, Jef and Coopman, Frank and Duchateau, Luc and Van Wallendael, Glenn and Hoecke, Sofie},
    year = {2025},
    month = {03},
    title = {Reliable uncertainty quantification for 2D/3D anatomical landmark localization using multi-output conformal prediction},
    archivePrefix={arXiv},
    doi = {10.48550/arXiv.2503.14106}
}

@misc{cardoso2022monaiopensourceframeworkdeep,
      title={MONAI: An open-source framework for deep learning in healthcare}, 
      author={M. Jorge Cardoso and Wenqi Li and Richard Brown and Nic Ma and Eric Kerfoot and Yiheng Wang and Benjamin Murrey and Andriy Myronenko and Can Zhao and Dong Yang and Vishwesh Nath and Yufan He and Ziyue Xu and Ali Hatamizadeh and Andriy Myronenko and Wentao Zhu and Yun Liu and Mingxin Zheng and Yucheng Tang and Isaac Yang and Michael Zephyr and Behrooz Hashemian and Sachidanand Alle and Mohammad Zalbagi Darestani and Charlie Budd and Marc Modat and Tom Vercauteren and Guotai Wang and Yiwen Li and Yipeng Hu and Yunguan Fu and Benjamin Gorman and Hans Johnson and Brad Genereaux and Barbaros S. Erdal and Vikash Gupta and Andres Diaz-Pinto and Andre Dourson and Lena Maier-Hein and Paul F. Jaeger and Michael Baumgartner and Jayashree Kalpathy-Cramer and Mona Flores and Justin Kirby and Lee A. D. Cooper and Holger R. Roth and Daguang Xu and David Bericat and Ralf Floca and S. Kevin Zhou and Haris Shuaib and Keyvan Farahani and Klaus H. Maier-Hein and Stephen Aylward and Prerna Dogra and Sebastien Ourselin and Andrew Feng},
      year={2022},
      eprint={2211.02701},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2211.02701}, 
}

@article{he2024anchor,
  title={Anchor Ball Regression Model for large-scale 3D skull landmark detection},
  author={He, Tao and Xu, Guikun and Cui, Li and Tang, Wei and Long, Jie and Guo, Jixiang},
  journal={Neurocomputing},
  volume={567},
  pages={127051},
  year={2024},
  publisher={Elsevier}
}

% other work
@inproceedings{wang2024dl2g,
  title={DL2G: Anatomical Landmark Detection with Deep Local Features and Geometric Global Constraint},
  author={Wang, Rui and Yang, Wanli and Xiao, Kuntao and Sun, Yi and Sheng, Shurong and Lv, Zhao and Gao, Jiahong},
  booktitle={2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
  pages={1695--1700},
  year={2024},
  organization={IEEE}
}

@article{salari2024reliability,
  title={Reliability of deep learning models for anatomical landmark detection: The role of inter-rater variability},
  author={Salari, Soorena and Rivaz, Hassan and Xiao, Yiming},
  journal={arXiv preprint arXiv:2411.17850},
  year={2024}
}

% dataset afids
@article{taha2023magnetic,
  title={Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration},
  author={Taha, Alaa and Gilmore, Greydon and Abbass, Mohamad and Kai, Jason and Kuehn, Tristan and Demarco, John and Gupta, Geetika and Zajner, Chris and Cao, Daniel and Chevalier, Ryan and others},
  journal={Scientific Data},
  volume={10},
  number={1},
  pages={449},
  year={2023},
  publisher={Nature Publishing Group UK London}
}

@article{abbass2022application,
  title={Application of the anatomical fiducials framework to a clinical dataset of patients with Parkinson’s disease},
  author={Abbass, Mohamad and Gilmore, Greydon and Taha, Alaa and Chevalier, Ryan and Jach, Magdalena and Peters, Terry M and Khan, Ali R and Lau, Jonathan C},
  journal={Brain Structure and Function},
  pages={1--13},
  year={2022},
  publisher={Springer}
}

%subdataset OASIS
@article{marcus2007open,
  title={Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults},
  author={Marcus, Daniel S and Wang, Tracy H and Parker, Jamie and Csernansky, John G and Morris, John C and Buckner, Randy L},
  journal={Journal of cognitive neuroscience},
  volume={19},
  number={9},
  pages={1498--1507},
  year={2007},
  publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…}
}

%subset LHSCPD
@dataset{ds004471:1.0.1,
  author = {Mohamad Abbass and Greydon Gilmore and Alaa Taha and Ryan Chevalier and Magdalena Jach and Terry M. Peters and Ali R. Khan and Jonathan C. Lau},
  title = {"London Heath Sciences Center Parkinson's Disease Dataset (LHSCPD)"},
  year = {2023},
  doi = {doi:10.18112/openneuro.ds004471.v1.0.1},
  publisher = {OpenNeuro}
}

%subset SNSX
@dataset{ds004470:1.0.1,
  author = {Jonathan C. Lau and Yiming Xiao and Roy A. M. Haast and Greydon Gilmore and Kâmil Uludağ and Keith W. MacDougall and Ravi S. Menon and Andrew G. Parrent and Terry M. Peters and Ali R. Khan},
  title = {"Stereotactic Neurosurgery Dataset (SNSX)"},
  year = {2023},
  doi = {doi:10.18112/openneuro.ds004470.v1.0.1},
  publisher = {OpenNeuro}
}

%subset HCP
@article{van2012human,
  title={The Human Connectome Project: a data acquisition perspective},
  author={Van Essen, David C and Ugurbil, Kamil and Auerbach, Edward and Barch, Deanna and Behrens, Timothy EJ and Bucholz, Richard and Chang, Acer and Chen, Liyong and Corbetta, Maurizio and Curtiss, Sandra W and others},
  journal={Neuroimage},
  volume={62},
  number={4},
  pages={2222--2231},
  year={2012},
  publisher={Elsevier}
}

% DMGLD LFC dataset
@article{Gong2025DMGLD, title={Domain Generalized Medical Landmark Detection via Robust Boundary-Aware Pre-Training}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/32323}, DOI={10.1609/aaai.v39i3.32323}, number={3}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Gong, Haifan and Lu, Yu and Wan, Xiang and Li, Haofeng}, year={2025}, month={Apr.}, pages={3140-3148} }

@ARTICLE{Gong2025MethodBenchmark,
  author={Gong, Haifan and Liu, Huixian and Wang, Yitao and Liu, Xiaoling and Wan, Xiang and Shi, Qiao and Li, Haofeng},
  journal={IEEE Journal of Biomedical and Health Informatics}, 
  title={Fetal Cerebellum Landmark Detection Based on 3D MRI: Method and Benchmark}, 
  year={2025},
  volume={29},
  number={8},
  pages={5712-5721},
  keywords={Cerebellum;Magnetic resonance imaging;Three-dimensional displays;Benchmark testing;Ultrasonic imaging;Biometrics;Biomedical imaging;Transformers;Training;Annotations;Fetal cerebellum;landmark detection;transformer;pseudo-label;multi-task learning;attention modeling;magnetic resonance imaging;benchmark},
  doi={10.1109/JBHI.2025.3559702}}

@INPROCEEDINGS{Gong2025nnmamba,
  author={Gong, Haifan and Kang, Luoyao and Wang, Yitao and Wang, Yihan and Wan, Xiang and Wu, Xusheng and Li, Haofeng},
  booktitle={2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI)}, 
  title={Nnmamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model}, 
  year={2025},
  volume={},
  number={},
  pages={1-5},
  keywords={Learning systems;Solid modeling;Image segmentation;Three-dimensional displays;Image analysis;Computational modeling;Biological system modeling;Computer architecture;Transformers;Biomedical imaging;Medical Image Segmentation;Classification;Landmark Detection;State Space Model},
  doi={10.1109/ISBI60581.2025.10980694}}

% Fetal pose estimation dataset
@INPROCEEDINGS{Chen2020fetuspose,
  author={Chen, Chaoyu and Yang, Xin and Huang, Ruobing and Shi, Wenlong and Liu, Shengfeng and Lin, Mingrong and Huang, Yuhao and Yang, Yong and Zhang, Yuanji and Luo, Huanjia and Huang, Yankai and Xiong, Yi and Ni, Dong},
  booktitle={2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)}, 
  title={Region Proposal Network with Graph Prior and Iou-Balance Loss for Landmark Detection in 3D Ultrasound}, 
  year={2020},
  volume={},
  number={},
  pages={1-5},
  keywords={Three-dimensional displays;Ultrasonic imaging;Task analysis;Object detection;Proposals;Face;Biomedical imaging;3D ultrasound;Landmark detection;Region proposal network;Fetal face;Prior knowledge},
  doi={10.1109/ISBI45749.2020.9098368}}

@article{Chen2024FetusMapV2,
title = {FetusMapV2: Enhanced fetal pose estimation in 3D ultrasound},
journal = {Medical Image Analysis},
volume = {91},
pages = {103013},
year = {2024},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2023.103013},
url = {https://www.sciencedirect.com/science/article/pii/S1361841523002736},
author = {Chaoyu Chen and Xin Yang and Yuhao Huang and Wenlong Shi and Yan Cao and Mingyuan Luo and Xindi Hu and Lei Zhu and Lequan Yu and Kejuan Yue and Yuanji Zhang and Yi Xiong and Dong Ni and Weijun Huang},
keywords = {3D ultrasound, Pose estimation, GPU memory management, Pair loss, Self-supervised learning},
}

% PDDCA dataset
@article{Raudaschl2017PDDCA,
author = {Raudaschl, Patrik F. and Zaffino, Paolo and Sharp, Gregory C. and Spadea, Maria Francesca and Chen, Antong and Dawant, Benoit M. and Albrecht, Thomas and Gass, Tobias and Langguth, Christoph and Lüthi, Marcel and Jung, Florian and Knapp, Oliver and Wesarg, Stefan and Mannion-Haworth, Richard and Bowes, Mike and Ashman, Annaliese and Guillard, Gwenael and Brett, Alan and Vincent, Graham and Orbes-Arteaga, Mauricio and Cárdenas-Peña, David and Castellanos-Dominguez, German and Aghdasi, Nava and Li, Yangming and Berens, Angelique and Moe, Kris and Hannaford, Blake and Schubert, Rainer and Fritscher, Karl D.},
title = {Evaluation of segmentation methods on head and neck CT: Auto-segmentation challenge 2015},
journal = {Medical Physics},
volume = {44},
number = {5},
pages = {2020-2036},
keywords = {atlas-based segmentation, automated segmentation, model-based segmentation, segmentation challenge},
doi = {https://doi.org/10.1002/mp.12197},
year = {2017}
}

% FeTA21 test dataset
@article{Payette2021FeTA21,
  author       = {Payette, Kelly and de Dumast, Priscille and Kebiri, Hamza and Ezhov, Ivan and Paetzold, Johannes C. and Shit, Suprosanna and Iqbal, Asim and Khan, Romesa and Kottke, Raimund and Grehten, Patrice and Ji, Hui and Lanczi, Levente and Nagy, Marianna and Beresova, Monika and Nguyen, Thi Dao and Natalucci, Giancarlo and Karayannis, Theofanis and Menze, Bjoern and Bach Cuadra, Meritxell and Jakab, Andras},
  title        = {An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset},
  journaltitle  = {Scientific Data},
  date         = {2021-07-06},
  volume       = {8},
  number       = {1},
  pages        = {167},
  doi          = {10.1038/s41597-021-00946-3},
  url          = {https://doi.org/10.1038/s41597-021-00946-3},
  issn         = {2052-4463},
}

@misc{Sanchez2024FeTA_Biometry,
  author       = {Sanchez, Thomas and
                  Gomez, Yvan and
                  Licandro, Roxane and
                  Payette, Kelly and
                  Jakab, Andras and
                  Koob, Meriam and
                  Bach Cuadra, Meritxell},
  title        = {Fetal Tissue Annotation Challenge (FeTA) Biometry
                   - MICCAI 2024
                  },
  month        = may,
  year         = 2024,
  publisher    = {Zenodo},
  version      = 1,
  doi          = {10.5281/zenodo.11192452},
  url          = {https://doi.org/10.5281/zenodo.11192452},
}


% Motivation
@article{schwendicke2021deep,
  title={Deep learning for cephalometric landmark detection: systematic review and meta-analysis},
  author={Schwendicke, Falk and Chaurasia, Akhilanand and Arsiwala, Lubaina and Lee, Jae-Hong and Elhennawy, Karim and Jost-Brinkmann, Paul-Georg and Demarco, Flavio and Krois, Joachim},
  journal={Clinical oral investigations},
  volume={25},
  number={7},
  pages={4299--4309},
  year={2021},
  publisher={Springer}
}

@article{serafin2023accuracy,
  title={Accuracy of automated 3D cephalometric landmarks by deep learning algorithms: systematic review and meta-analysis},
  author={Serafin, Marco and Baldini, Benedetta and Cabitza, Federico and Carrafiello, Gianpaolo and Baselli, Giuseppe and Del Fabbro, Massimo and Sforza, Chiarella and Caprioglio, Alberto and Tartaglia, Gianluca M},
  journal={La radiologia medica},
  volume={128},
  number={5},
  pages={544--555},
  year={2023},
  publisher={Springer}
}

@article{singh20203d,
  title={3D deep learning on medical images: a review},
  author={Singh, Satya P and Wang, Lipo and Gupta, Sukrit and Goli, Haveesh and Padmanabhan, Parasuraman and Guly{\'a}s, Bal{\'a}zs},
  journal={Sensors},
  volume={20},
  number={18},
  pages={5097},
  year={2020},
  publisher={MDPI}
}

% related work specialized methods on only 1 dataset
@article{chen2021fast,
  title={Fast and accurate craniomaxillofacial landmark detection via 3D faster R-CNN},
  author={Chen, Xiaoyang and Lian, Chunfeng and Deng, Hannah H and Kuang, Tianshu and Lin, Hung-Ying and Xiao, Deqiang and Gateno, Jaime and Shen, Dinggang and Xia, James J and Yap, Pew-Thian},
  journal={IEEE transactions on medical imaging},
  volume={40},
  number={12},
  pages={3867--3878},
  year={2021},
  publisher={IEEE}
}

@article{chen2022structure,
  title={Structure-aware long short-term memory network for 3D cephalometric landmark detection},
  author={Chen, Runnan and Ma, Yuexin and Chen, Nenglun and Liu, Lingjie and Cui, Zhiming and Lin, Yanhong and Wang, Wenping},
  journal={IEEE Transactions on Medical Imaging},
  volume={41},
  number={7},
  pages={1791--1801},
  year={2022},
  publisher={IEEE}
}

@article{tao2024craniomaxillofacial,
  title={Craniomaxillofacial landmarks detection in CT scans with limited labeled data via semi-supervised learning},
  author={Tao, Leran and Zhang, Xu and Yang, Yang and Cheng, Mengjia and Zhang, Rongbin and Qian, Hongjun and Wen, Yaofeng and Yu, Hongbo},
  journal={Heliyon},
  volume={10},
  number={14},
  year={2024},
  publisher={Elsevier}
}

% Methods
@inproceedings{pfister2015flowing,
  title={Flowing convnets for human pose estimation in videos},
  author={Pfister, Tomas and Charles, James and Zisserman, Andrew},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={1913--1921},
  year={2015}
}

@inproceedings{payer2016regressing,
  title={Regressing heatmaps for multiple landmark localization using CNNs},
  author={Payer, Christian and {\v{S}}tern, Darko and Bischof, Horst and Urschler, Martin},
  booktitle={International conference on medical image computing and computer-assisted intervention},
  pages={230--238},
  year={2016},
  organization={Springer}
}
@inproceedings{lee2017cephalometric,
  title={Cephalometric landmark detection in dental x-ray images using convolutional neural networks},
  author={Lee, Hansang and Park, Minseok and Kim, Junmo},
  booktitle={Medical imaging 2017: Computer-aided diagnosis},
  volume={10134},
  pages={494--499},
  year={2017},
  organization={SPIE}
}

@article{song2021efficient,
  title={An efficient deep learning based coarse-to-fine cephalometric landmark detection method},
  author={Song, Yu and Qiao, Xu and Iwamoto, Yutaro and Chen, Yen-wei and Chen, Yili},
  journal={IEICE TRANSACTIONS on Information and Systems},
  volume={104},
  number={8},
  pages={1359--1366},
  year={2021},
  publisher={The Institute of Electronics, Information and Communication Engineers}
}

% Figure 1 publications
@article{cui2025pseudo,
  title={A pseudo-3D coarse-to-fine architecture for 3D medical landmark detection},
  author={Cui, Li and Liu, Boyan and Xu, Guikun and Guo, Jixiang and Tang, Wei and He, Tao},
  journal={Neurocomputing},
  volume={614},
  pages={128782},
  year={2025},
  publisher={Elsevier}
}

@article{liu2023joint,
  title={Joint cranial bone labeling and landmark detection in pediatric CT images using context encoding},
  author={Liu, Jiawei and Xing, Fuyong and Shaikh, Abbas and French, Brooke and Linguraru, Marius George and Porras, Antonio R},
  journal={IEEE transactions on medical imaging},
  volume={42},
  number={10},
  pages={3117--3126},
  year={2023},
  publisher={IEEE}
}

@InProceedings{pang2024prior,
  title = 	 {Prior Guided 3D Medical Image Landmark Localization},
  author =       {Pang, Yijie and Cheng, Pujin and Lyu, Junyan and Lin, Fan and Tang, Xiaoying},
  booktitle = 	 {Medical Imaging with Deep Learning},
  pages = 	 {1163--1175},
  year = 	 {2024},
  editor = 	 {Oguz, Ipek and Noble, Jack and Li, Xiaoxiao and Styner, Martin and Baumgartner, Christian and Rusu, Mirabela and Heinmann, Tobias and Kontos, Despina and Landman, Bennett and Dawant, Benoit},
  volume = 	 {227},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {10--12 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v227/pang24a/pang24a.pdf},
  url = 	 {https://proceedings.mlr.press/v227/pang24a.html},
}

@article{barough2025brainsignsnet,
  title={BrainSignsNET: Deep Learning-Based 3D Anatomical Landmark Detection in Human Brain Imaging},
  author={Barough, Siavash Shirzadeh and Ventura, Catalina and Bilgel, Murat and Albert, Marilyn S and Miller, Michael I and Moghekar, Abhay},
  journal={medRxiv},
  pages={2025--07},
  year={2025},
  publisher={Cold Spring Harbor Laboratory Press}
}

 % chen 2022 had already ref

@article{kang2021CephReinforcement,
  title={3D cephalometric landmark detection by multiple stage deep reinforcement learning},
  author={Kang, Sung Ho and Jeon, Kiwan and Kang, Sang-Hoon and Lee, Sang-Hwy},
  journal={Scientific reports},
  volume={11},
  number={1},
  pages={17509},
  year={2021},
  publisher={Nature Publishing Group UK London}
}

@inproceedings{jiang2022cephalformer,
  title={Cephalformer: Incorporating global structure constraint into visual features for general cephalometric landmark detection},
  author={Jiang, Yankai and Li, Yiming and Wang, Xinyue and Tao, Yubo and Lin, Jun and Lin, Hai},
  booktitle={International conference on medical image computing and computer-assisted intervention},
  pages={227--237},
  year={2022},
  organization={Springer}
}

@inproceedings{shi2025EndTooth,
  title={End-to-End 3D Tooth Landmark Detection with Fuzzy Tooth Localization},
  author={Shi, Kaibo and Jin, Hairong and Zheng, Youyi},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={170--180},
  year={2025},
  organization={Springer}
}

@article{dai2024NasalBone,
  title={Cone-beam CT landmark detection for measuring basal bone width: a retrospective validation study},
  author={Dai, Juan and Guo, Xinge and Zhang, Hongyuan and Xie, Haoyu and Huang, Jiahui and Huang, Qiangtai and Huang, Bingsheng},
  journal={BMC Oral Health},
  volume={24},
  number={1},
  pages={1091},
  year={2024},
  publisher={Springer}
}

@article{stebani2023InnerEar,
  title={Towards fully automated inner ear analysis with deep-learning-based joint segmentation and landmark detection framework},
  author={Stebani, Jannik and Blaimer, Martin and Zabler, Simon and Neun, Tilmann and Pelt, Dani{\"e}l M and Rak, Kristen},
  journal={Scientific Reports},
  volume={13},
  number={1},
  pages={19057},
  year={2023},
  publisher={Nature Publishing Group UK London}
}

@article{baksi2021SoftTissue,
  title={Accuracy of an automated method of 3D soft tissue landmark detection},
  author={Baksi, Sanjana and Freezer, Simon and Matsumoto, Takeshi and Dreyer, Craig},
  journal={European journal of orthodontics},
  volume={43},
  number={6},
  pages={622--630},
  year={2021},
  publisher={Oxford University Press UK}
}

@article{gillot2023cbct,
  title={Automatic landmark identification in cone-beam computed tomography},
  author={Gillot, Maxime and Miranda, Felicia and Baquero, Baptiste and Ruellas, Antonio and Gurgel, Marcela and Al Turkestani, Najla and Anchling, Luc and Hutin, Nathan and Biggs, Elizabeth and Yatabe, Marilia and others},
  journal={Orthodontics \& craniofacial research},
  volume={26},
  number={4},
  pages={560--567},
  year={2023},
  publisher={Wiley Online Library}
}

@inproceedings{salari2023uncertainty,
  title={Uncertainty-aware transformer model for anatomical landmark detection in paraspinal muscle MRIs},
  author={Salari, Soorena and Rasoulian, Amirhossein and Battie, Michele and Fortin, Maryse and Rivaz, Hassan and Xiao, Yiming},
  booktitle={Medical Imaging 2023: Image Processing},
  volume={12464},
  pages={246--252},
  year={2023},
  organization={SPIE}
}

@ARTICLE{wu2022MeshTooth,
  author={Wu, Tai-Hsien and Lian, Chunfeng and Lee, Sanghee and Pastewait, Matthew and Piers, Christian and Liu, Jie and Wang, Fan and Wang, Li and Chiu, Chiung-Ying and Wang, Wenchi and Jackson, Christina and Chao, Wei-Lun and Shen, Dinggang and Ko, Ching-Chang},
  journal={IEEE Transactions on Medical Imaging}, 
  title={Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans}, 
  year={2022},
  volume={41},
  number={11},
  pages={3158-3166},
  keywords={Teeth;Dentistry;Three-dimensional displays;Location awareness;Task analysis;Solid modeling;Heating systems;Tooth segmentation;anatomical landmark detection;orthodontic treatment planning;3D deep learning;intraoral scan},
  doi={10.1109/TMI.2022.3180343}}

@ARTICLE{Li2023SpatialKnee,
  author={Li, Xiang and Lv, Songcen and Li, Minglei and Zhang, Jiusi and Jiang, Yuchen and Qin, Yong and Luo, Hao and Yin, Shen},
  journal={IEEE Transactions on Medical Imaging}, 
  title={SDMT: Spatial Dependence Multi-Task Transformer Network for 3D Knee MRI Segmentation and Landmark Localization}, 
  year={2023},
  volume={42},
  number={8},
  pages={2274-2285},
  keywords={Task analysis;Image segmentation;Location awareness;Ligaments;Bones;Three-dimensional displays;Magnetic resonance imaging;Knee segmentation;Knee landmark localization;convolutional neural network;transformer;multi-task learning},
  doi={10.1109/TMI.2023.3247543}}

@article{lu2023cmf,
  title={CMF-Net: craniomaxillofacial landmark localization on CBCT images using geometric constraint and transformer},
  author={Lu, Gang and Shu, Huazhong and Bao, Han and Kong, Youyong and Zhang, Chen and Yan, Bin and Zhang, Yuanxiu and Coatrieux, Jean-Louis},
  journal={Physics in Medicine \& Biology},
  volume={68},
  number={9},
  pages={095020},
  year={2023},
  publisher={IOP Publishing}
}

@ARTICLE{Lang2022CraniomaxillofacialMaskRCNN,
  author={Lang, Yankun and Lian, Chunfeng and Xiao, Deqiang and Deng, Hannah and Thung, Kim-Han and Yuan, Peng and Gateno, Jaime and Kuang, Tianshu and Alfi, David M. and Wang, Li and Shen, Dinggang and Xia, James J. and Yap, Pew-Thian},
  journal={IEEE Transactions on Medical Imaging}, 
  title={Localization of Craniomaxillofacial Landmarks on CBCT Images Using 3D Mask R-CNN and Local Dependency Learning}, 
  year={2022},
  volume={41},
  number={10},
  pages={2856-2866},
  keywords={Proposals;Location awareness;Surgery;Biomedical imaging;Task analysis;Feature extraction;Three-dimensional displays;Craniomaxilloficial (CMF) landmark localization;deep learning;Mask R-CNN},
  doi={10.1109/TMI.2022.3174513}}

@inproceedings{lopez2021facial,
  title={Facial and cochlear nerves characterization using deep reinforcement learning for landmark detection},
  author={L{\'o}pez Diez, Paula and Sundgaard, Josefine Vilsb{\o}ll and Patou, Fran{\c{c}}ois and Margeta, Jan and Paulsen, Rasmus Reinhold},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={519--528},
  year={2021},
  organization={Springer}
}

@article{zhu2022novel,
  title={A novel method for 3D knee anatomical landmark localization by combining global and local features},
  author={Zhu, Junjun and Zhao, Qijie and Zhu, Junhao and Zhou, Anwen and Shao, Hui},
  journal={Machine Vision and Applications},
  volume={33},
  number={4},
  pages={52},
  year={2022},
  publisher={Springer}
}

@inproceedings{lang2022dentalpointnet,
  title={DentalPointNet: landmark localization on high-resolution 3D digital dental models},
  author={Lang, Yankun and Chen, Xiaoyang and Deng, Hannah H and Kuang, Tianshu and Barber, Joshua C and Gateno, Jaime and Yap, Pew-Thian and Xia, James J},
  booktitle={International conference on medical image computing and computer-assisted intervention},
  pages={444--452},
  year={2022},
  organization={Springer}
}

@article{dot2022automatic,
  title={Automatic 3-dimensional cephalometric landmarking via deep learning},
  author={Dot, Gauthier and Schouman, Thomas and Chang, Shaole and Rafflenbeul, Fr{\'e}d{\'e}ric and Kerbrat, Adeline and Rouch, Philippe and Gajny, Laurent},
  journal={Journal of dental research},
  volume={101},
  number={11},
  pages={1380--1387},
  year={2022},
  publisher={SAGE Publications Sage CA: Los Angeles, CA}
}

@article{torosdagli2023relational,
  title={Relational reasoning network for anatomical landmarking},
  author={Torosdagli, Neslisah and Anwar, Syed and Verma, Payal and Liberton, Denise K and Lee, Janice S and Han, Wade W and Bagci, Ulas},
  journal={Journal of Medical Imaging},
  volume={10},
  number={2},
  pages={024002--024002},
  year={2023},
  publisher={Society of Photo-Optical Instrumentation Engineers}
}

