NSTL国家科技图书文献中心

1. Automated Dental CBCT Segmentation Using Pseudo Labeling Method NSTL国家科技图书文献中心

Weiyan Feng - 《Semi-supervised Tooth Segmentation》 - MICCAI Challenge on Semi-supervised Tooth Segmentation | International Conference on Medical Image Computing and Computer Assisted Intervention - 2025, - 156～168 - 共13页

摘要： segmentation in contemporary artificial intelligence-assisted | diagnosis is growing. Accurate teeth segmentation* can* | the 3D CBCT segmentation* task and design a semi* | teeth segmentation* on the validation set of CTooth+.* | The significance of 3D medical image

关键词： Medical segmentation | Pseudo labeling method | 3D segmentation

2. Perform Special Post-processing After Tooth Segmentation NSTL国家科技图书文献中心

Bing Wang | Chi Zhang ... - 《Semi-supervised Tooth Segmentation》 - MICCAI Challenge on Semi-supervised Tooth Segmentation | International Conference on Medical Image Computing and Computer Assisted Intervention - 2025, - 25～35 - 共11页

摘要： segmentation of these images stands out as a pivotal task | refined segmentation* process, practitioners are* | the segmentation* outcomes. As a tangible testament* | enhancing the precision of dental image segmentation | In the ever-evolving realm of dental imaging

关键词： Tooth segmentation | Prior knowledge | NnU-Net

3. TB-FPN: Enhancing Tooth Segmentation with Cascade Boundary-Aware FPN NSTL国家科技图书文献中心

Xinxu Cai | Yisong Zhang ... - 《Semi-supervised Tooth Segmentation》 - MICCAI Challenge on Semi-supervised Tooth Segmentation | International Conference on Medical Image Computing and Computer Assisted Intervention - 2025, - 14～24 - 共11页

摘要： for effective tooth segmentation* greatly simplifies* | applied to tooth image segmentation. This method aims to | significantly improve the segmentation* capability of tooth* | addressing the challenges in tooth segmentation* tasks. We* | segmentation. The average Dice score is 91.75%, the average

关键词： Segmentation | TB-FPN | Dental panoramic radiographs

4. UX-CNet: Effective Edge Information Acquisition for Teeth Image Segmentation NSTL国家科技图书文献中心

Hao Leng | Lianghuang Huang - 《Semi-supervised Tooth Segmentation》 - MICCAI Challenge on Semi-supervised Tooth Segmentation | International Conference on Medical Image Computing and Computer Assisted Intervention - 2025, - 133～144 - 共12页

摘要： annotation, poor segmentation* of the edges of the teeth* | effect of teeth edge segmentation. In the experiment | segmentation and detail segmentation. Our method achieved an | 0.03 for the teeth segmentation* on the validation* | Currently, the dataset of children's teeth

关键词： Tooth segmentation | UX-CNet | Data augmentation | Deep learning

5. High-Precision Semi-supervised 3D Dental Segmentation Based on nnUNet NSTL国家科技图书文献中心

Bingyan Zhang | Xuefei Zhu - 《Semi-supervised Tooth Segmentation》 - MICCAI Challenge on Semi-supervised Tooth Segmentation | International Conference on Medical Image Computing and Computer Assisted Intervention - 2025, - 180～191 - 共12页

摘要： segmentation based on 3D Cone-Beam Computed Tomography (CBCT | segmentation of the teeth is crucial for dental diagnosis and | segmentation method suitable for 3D CBCT data. This | 2023 Challenge: STS-3D CBCT-based tooth segmentation | In the field of medical imaging, tooth

关键词： Semi-supervised learning | Tooth segmentation | MICCAI STS challenge

6. Boundary Feature Fusion Network for Tooth Image Segmentation NSTL国家科技图书文献中心

Dongping Zhang | Zheng Li ... - 《Semi-supervised Tooth Segmentation》 - MICCAI Challenge on Semi-supervised Tooth Segmentation | International Conference on Medical Image Computing and Computer Assisted Intervention - 2025, - 110～123 - 共14页

摘要：Tooth segmentation* is a critical technology in* | the field of medical image segmentation, with | development of numerous tooth image segmentation* models by* | segmentation network that integrates boundary information to | results in precise tooth segmentation. In the most

关键词： Tooth segmentation | Boundary information | Boundary feature extraction | Feature cross-fusion

7. Interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound Images NSTL国家科技图书文献中心

Hao Li | Baris Oguz ... - 《Simplifying Medical Ultrasound》 - International Workshop on Advances in Simplifying Medical Ultrasound | International Conference on Medical Image Computing and Computer Assisted Intervention - 2025, - 132～142 - 共11页

摘要： consuming. Automated segmentation* algorithms can often* | segmentation models have been widely applied in the medical | imaging domain. These models produce a segmentation* from* | available state-of-the-art 3D interactive segmentation | the placenta segmentation* task. The Dice score*

关键词： Interactive segmentation | Scribbles | Deep learning | Placenta segmentation | 3D ultrasound (3DUS) image

8. Preprocessing of Prior Knowledge Before Semi-supervised Tooth Segmentation NSTL国家科技图书文献中心

Bing Wang | Chi Zhang ... - 《Semi-supervised Tooth Segmentation》 - MICCAI Challenge on Semi-supervised Tooth Segmentation | International Conference on Medical Image Computing and Computer Assisted Intervention - 2025, - 46～57 - 共12页

摘要： precise segmentation* of teeth, as it provides vital* | medical image segmentation, the practical integration of | tooth segmentation. A key insight from our research is | In the realm of dental imaging, the | utilisation of 3D dental cone-beam computed tomography (CBCT

关键词： Tooth segmentation | Prior knowledge | Pseudo-label | Semi-supervision

9. Semi-supervised 3D Tooth Segmentation Using nn-UNet with Axial Attention and Positional Correction NSTL国家科技图书文献中心

Qiupu Chen | Yimou Wang ... - 《Semi-supervised Tooth Segmentation》 - MICCAI Challenge on Semi-supervised Tooth Segmentation | International Conference on Medical Image Computing and Computer Assisted Intervention - 2025, - 96～109 - 共14页

摘要：Accurate 3D tooth segmentation* plays a pivotal* | MICCAI 2023 semi-supervised teeth segmentation | segmentation through the application of deep learning within | innovative modifications to improve segmentation | segmentation accuracy. Simultaneously, the positional

关键词： Tooth segmentation | Image segmentation | Semi-supervised learning | Deep learning | CBCT image

10. Occlusion-Aware Seamless Segmentation NSTL国家科技图书文献中心

Yihong Cao | Jiaming Zhang ... - 《Computer Vision - ECCV 2024,Part XIX》 - European Conference on Computer Vision - 2025, - 129～147 - 共19页

摘要： novel task, Occlusion-Aware Seamless Segmentation | Segmentation, i.e., BlendPASS. Besides, we propose the first | segmentation datasets, i.e., SynPASS and DenseP-ASS, our | Panoramic images can broaden the Field of View | (FoV), occlusion-aware prediction can deepen the

摘要： segmentation in contemporary artificial intelligence-assisted | diagnosis is growing. Accurate teeth segmentation* can* | the 3D CBCT segmentation* task and design a semi* | teeth segmentation* on the validation set of CTooth+.* | The significance of 3D medical image

关键词： Medical segmentation | Pseudo labeling method | 3D segmentation

摘要： segmentation of these images stands out as a pivotal task | refined segmentation* process, practitioners are* | the segmentation* outcomes. As a tangible testament* | enhancing the precision of dental image segmentation | In the ever-evolving realm of dental imaging

关键词： Tooth segmentation | Prior knowledge | NnU-Net

关键词： Segmentation | TB-FPN | Dental panoramic radiographs

摘要： annotation, poor segmentation* of the edges of the teeth* | effect of teeth edge segmentation. In the experiment | segmentation and detail segmentation. Our method achieved an | 0.03 for the teeth segmentation* on the validation* | Currently, the dataset of children's teeth

关键词： Tooth segmentation | UX-CNet | Data augmentation | Deep learning

摘要： segmentation based on 3D Cone-Beam Computed Tomography (CBCT | segmentation of the teeth is crucial for dental diagnosis and | segmentation method suitable for 3D CBCT data. This | 2023 Challenge: STS-3D CBCT-based tooth segmentation | In the field of medical imaging, tooth

关键词： Semi-supervised learning | Tooth segmentation | MICCAI STS challenge

摘要：Tooth segmentation* is a critical technology in* | the field of medical image segmentation, with | development of numerous tooth image segmentation* models by* | segmentation network that integrates boundary information to | results in precise tooth segmentation. In the most

关键词： Tooth segmentation | Boundary information | Boundary feature extraction | Feature cross-fusion

摘要： consuming. Automated segmentation* algorithms can often* | segmentation models have been widely applied in the medical | imaging domain. These models produce a segmentation* from* | available state-of-the-art 3D interactive segmentation | the placenta segmentation* task. The Dice score*

关键词： Interactive segmentation | Scribbles | Deep learning | Placenta segmentation | 3D ultrasound (3DUS) image

摘要： precise segmentation* of teeth, as it provides vital* | medical image segmentation, the practical integration of | tooth segmentation. A key insight from our research is | In the realm of dental imaging, the | utilisation of 3D dental cone-beam computed tomography (CBCT

关键词： Tooth segmentation | Prior knowledge | Pseudo-label | Semi-supervision

摘要：Accurate 3D tooth segmentation* plays a pivotal* | MICCAI 2023 semi-supervised teeth segmentation | segmentation through the application of deep learning within | innovative modifications to improve segmentation | segmentation accuracy. Simultaneously, the positional

关键词： Tooth segmentation | Image segmentation | Semi-supervised learning | Deep learning | CBCT image

摘要： novel task, Occlusion-Aware Seamless Segmentation | Segmentation, i.e., BlendPASS. Besides, we propose the first | segmentation datasets, i.e., SynPASS and DenseP-ASS, our | Panoramic images can broaden the Field of View | (FoV), occlusion-aware prediction can deepen the

关键词： Panoramic scene understanding | Amodal segmentation

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： segmentation in contemporary artificial intelligence-assisted | diagnosis is growing. Accurate teeth segmentation can | the 3D CBCT segmentation task and design a semi | teeth segmentation on the validation set of CTooth+. | The significance of 3D medical image

关键词： Medical segmentation | Pseudo labeling method | 3D segmentation

摘要： segmentation of these images stands out as a pivotal task | refined segmentation process, practitioners are | the segmentation outcomes. As a tangible testament | enhancing the precision of dental image segmentation | In the ever-evolving realm of dental imaging

关键词： Tooth segmentation | Prior knowledge | NnU-Net

关键词： Segmentation | TB-FPN | Dental panoramic radiographs

摘要： annotation, poor segmentation of the edges of the teeth | effect of teeth edge segmentation. In the experiment | segmentation and detail segmentation. Our method achieved an | 0.03 for the teeth segmentation on the validation | Currently, the dataset of children's teeth

关键词： Tooth segmentation | UX-CNet | Data augmentation | Deep learning

摘要： segmentation based on 3D Cone-Beam Computed Tomography (CBCT | segmentation of the teeth is crucial for dental diagnosis and | segmentation method suitable for 3D CBCT data. This | 2023 Challenge: STS-3D CBCT-based tooth segmentation | In the field of medical imaging, tooth

关键词： Semi-supervised learning | Tooth segmentation | MICCAI STS challenge

摘要：Tooth segmentation is a critical technology in | the field of medical image segmentation, with | development of numerous tooth image segmentation models by | segmentation network that integrates boundary information to | results in precise tooth segmentation. In the most

关键词： Tooth segmentation | Boundary information | Boundary feature extraction | Feature cross-fusion

摘要： consuming. Automated segmentation algorithms can often | segmentation models have been widely applied in the medical | imaging domain. These models produce a segmentation from | available state-of-the-art 3D interactive segmentation | the placenta segmentation task. The Dice score

关键词： Interactive segmentation | Scribbles | Deep learning | Placenta segmentation | 3D ultrasound (3DUS) image

摘要： precise segmentation of teeth, as it provides vital | medical image segmentation, the practical integration of | tooth segmentation. A key insight from our research is | In the realm of dental imaging, the | utilisation of 3D dental cone-beam computed tomography (CBCT

关键词： Tooth segmentation | Prior knowledge | Pseudo-label | Semi-supervision

摘要：Accurate 3D tooth segmentation plays a pivotal | MICCAI 2023 semi-supervised teeth segmentation | segmentation through the application of deep learning within | innovative modifications to improve segmentation | segmentation accuracy. Simultaneously, the positional

关键词： Tooth segmentation | Image segmentation | Semi-supervised learning | Deep learning | CBCT image

摘要： novel task, Occlusion-Aware Seamless Segmentation | Segmentation, i.e., BlendPASS. Besides, we propose the first | segmentation datasets, i.e., SynPASS and DenseP-ASS, our | Panoramic images can broaden the Field of View | (FoV), occlusion-aware prediction can deepen the

关键词： Panoramic scene understanding | Amodal segmentation

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： segmentation in contemporary artificial intelligence-assisted | diagnosis is growing. Accurate teeth segmentation* can* | the 3D CBCT segmentation* task and design a semi* | teeth segmentation* on the validation set of CTooth+.* | The significance of 3D medical image

摘要： segmentation of these images stands out as a pivotal task | refined segmentation* process, practitioners are* | the segmentation* outcomes. As a tangible testament* | enhancing the precision of dental image segmentation | In the ever-evolving realm of dental imaging

摘要： annotation, poor segmentation* of the edges of the teeth* | effect of teeth edge segmentation. In the experiment | segmentation and detail segmentation. Our method achieved an | 0.03 for the teeth segmentation* on the validation* | Currently, the dataset of children's teeth

摘要：Tooth segmentation* is a critical technology in* | the field of medical image segmentation, with | development of numerous tooth image segmentation* models by* | segmentation network that integrates boundary information to | results in precise tooth segmentation. In the most

摘要： consuming. Automated segmentation* algorithms can often* | segmentation models have been widely applied in the medical | imaging domain. These models produce a segmentation* from* | available state-of-the-art 3D interactive segmentation | the placenta segmentation* task. The Dice score*

摘要： precise segmentation* of teeth, as it provides vital* | medical image segmentation, the practical integration of | tooth segmentation. A key insight from our research is | In the realm of dental imaging, the | utilisation of 3D dental cone-beam computed tomography (CBCT

摘要：Accurate 3D tooth segmentation* plays a pivotal* | MICCAI 2023 semi-supervised teeth segmentation | segmentation through the application of deep learning within | innovative modifications to improve segmentation | segmentation accuracy. Simultaneously, the positional