Titlebook: Semi-supervised Tooth Segmentation; First MICCAI Challen Yaqi Wang,Xiaodiao Chen,Hongyuan Zhang Conference proceedings 2025 The Editor(s) (

拖债

genuine

VEST

,High-Precision Semi-supervised 3D Dental Segmentation Based on nnUNet,STS 2023 Challenge: STS-3D CBCT-based tooth segmentation task, our method achieved a Dice similarity coefficient of 0.9111 and 0.7261, an IoU of 0.9164 and 0.7855, and a 3D Hausdorff distance of 0.0453 and 0.2595 on the preliminary and rematch test data set.

Cumulus

菊花

Conference proceedings 2025 64 submissions. The papers were written by participants in the STS challenge to describe their solutions for automatic teeth segmentation using the offcial training dataset released for this purpose...In general, this challenge aims to promote the development of the teeth segmentation in panoramic X-ray images and dental CBCT scans..

Congeal

,Convolutional Neural Network-Based Multi-scale Semantic Segmentation for Two-Dimensional Panoramic diographs. To enhance the model’s performance, we adopt a data augmentation strategy based on the combination of Mosaic and multi-scale image scaling, which significantly enriches the training set samples. Additionally, we propose a post-processing strategy based on the model’s prediction probabilit

institute

,TB-FPN: Enhancing Tooth Segmentation with Cascade Boundary-Aware FPN,urrence of misclassification and missed diagnoses, and improves the efficiency of medical work. To achieve this, we propose a Tooth Boundary-aware Feature Pyramid Network (TB-FPN), a semi-supervised deep learning method applied to tooth image segmentation. This method aims to significantly improve t

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