Semi-supervised Tooth Segmentation
Description
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.
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Content
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.
