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E-raamat: Semi-supervised Tooth Segmentation: First MICCAI Challenge, SemiToothSeg 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings

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This book constitutes the proceedings of the First MICCAI 2023 Challenge on Semi-supervised

Tooth Segmentation, SemiToothSeg 2023, held in Conjunction with MICCAI 2023, in Vancouver, BC, Canada, on October 8, 2023.





The 16 full papers presented in this book were carefully reviewed and selected from 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.
Convolutional Neural Network-based Multi-scale Semantic Segmentation for
Two-dimensional Panoramic X-rays of Teeth.- TB-FPN: Enhancing Tooth
Segmentation with Cascade Boundary-aware FPN.- Perform Special
Post-processing after Tooth Segmentation.- A Multi-Stage Framework for 3D
Individual Tooth Segmentation in Dental CBCT.- Preprocessing of Prior
Knowledge before Semi-Supervised Tooth Segmentation.- A Semi-Supervised Tooth
Segmentation Method based on Entropy-Guided Mean Teacher and Weakly Mutual
Consistency Network.- MsNet: Multi-Stage Learning from Seldom Labeled Data
for 3D Tooth Segmentation in Dental Cone Beam Computed Tomography.-
Diffusion-Based Conv-Former Dual-Encode U-Net: DDPM for Level Set Evolution
Mapping - MICCAI STS 2023 Challenge.- Semi-Supervised 3D Tooth Segmentation
Using nn-UNet with Axial Attention and Positional Correction.- Boundary
Feature Fusion Network for Tooth Image Segmentation.- Self-training Based
Semi-Supervised Learning and U-Net with Denoiser for Teeth Segmentation in
X-ray Image.- UX-CNet: Effective Edge Information Acquisition for Teeth Image
Segmentation.- 2D Teeth Segmentation Base on Half-image Approach and
VCMix-Net+.- Automated Dental CBCT Segmentation using Pseudo Labeling
Method.- Prior-aware Cross Pseudo Supervision for Semi-supervised Tooth
Segmentation.- High-Precision Semi-supervised 3D Dental Segmentation Based on
nnUNet.
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