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<p>Welcome to our dataset repository. Here, you'll find a collection of high-quality datasets that we've carefully collected and annotated. Please cite our paper if you use our dataset.</p>
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<p><strong>Please note:</strong> Our datasets are <em>only available for non-commercial use and academic research purposes</em>. Any commercial use is strictly prohibited.</p>
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<p class="welcome-text">
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Welcome to our dataset repository. Here, you'll find a collection of high-quality datasets that we've carefully collected and annotated. Please cite our paper if you use our dataset.
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</p>
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<div class="notice-box">
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<div class="notice-title">Important Notice</div>
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<div class="notice-content">
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<ul>
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<li>Our datasets are <span class="highlight-text">available only for non-commercial and academic research purposes</span>. Any form of commercial use is strictly prohibited.</li>
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<li>Access to all datasets is granted through an application and review process. To request access, please complete the <a href="./assets/data-request-form.pdf">Data Access Application Form</a> and email it to the first author of the corresponding paper and Dr. Zhiming Cui, while also cc your supervisor. Upon approval, you will receive the download link.</li>
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<div class="dataset-item">
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<h2>CBCT Dataset</h2>
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<p>This is the dataset for our 'A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images' paper in Nature Communication 2022. We released partial data (50 raw data of CBCT scans collected from dental clinics) to support the results in this study with permission from respective data centers. The full datasets are protected because of privacy issues and regulation policies in hospitals. </p>
title={A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images},
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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},
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journal={Nature communications},
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volume={13},
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number={1},
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pages={2096},
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year={2022},
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publisher={Nature Publishing Group UK London}
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title={A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images},
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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},
title={Cephalometric Landmark Detection across Ages with Prototypical Network},
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author={Wu, Han and Wang, Chong and Mei, Lanzhuju and Yang, Tong and Zhu, Min and Shen, Dinggang and Cui, Zhiming},
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booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
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pages={155--165},
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year={2024},
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organization={Springer}
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title={Cephalometric Landmark Detection across Ages with Prototypical Network},
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author={Wu, Han and Wang, Chong and Mei, Lanzhuju and Yang, Tong and Zhu, Min and Shen, Dinggang and Cui, Zhiming},
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booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
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pages={155--165},
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year={2024},
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organization={Springer}
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}</code></pre>
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<divclass="dataset-item">
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<h2>Tooth Alignment Dataset</h2>
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<p>We acquire 2,224 CBCT scans from Shanghai NinthPeople’s Hospital under consistent acquisition parameters (100 kVsource voltage, 0.3 mm voxel size, 468 × 468 × 250 resolution). Eachscan undergoes rigid registration to align it to a standardized jawcoordinate system for uniform orientation and field of view, and weapply stringent quality control to remove scans with metal or motionartifacts and incomplete dentition coverage. The resulting dataset comprises1,955 clinically validated high-quality sets of 3D tooth models.</p>
<div class="title">Fully Automated Evaluation of Condylar Remodeling After Orthognathic Surgery in Skeletal Class II Patients Using Deep Learning and Landmarks</div>
<div class="title">Fully Automated Evaluation of Condylar Remodeling After Orthognathic Surgery in Skeletal Class II Patients Using Deep Learning and Landmarks</div>
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