Semi-supervised Tooth Segmentation

First MICCAI Challenge, SemiToothSeg 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings

Häftad, Engelska, 2024

Av Yaqi Wang, Xiaodiao Chen, Dahong Qian, Fan Ye, Shuai Wang, Hongyuan Zhang

859 kr

Beställningsvara. Skickas inom 10-15 vardagar

Fri frakt för medlemmar vid köp för minst 249 kr.

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

Produktinformation

Utgivningsdatum2024-10-19
Mått155 x 235 x 12 mm
Vikt318 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor194
FörlagSpringer International Publishing AG
ISBN9783031723957

Tillhör följande kategorier

Tillämpad datateknik inom Data och it

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.