LEADER 03823nam 22005055 450 001 9910983360203321 005 20251113210017.0 010 $a9783031723964 010 $a3031723961 024 7 $a10.1007/978-3-031-72396-4 035 $a(CKB)36382890600041 035 $a(DE-He213)978-3-031-72396-4 035 $a(MiAaPQ)EBC31733333 035 $a(Au-PeEL)EBL31733333 035 $a(EXLCZ)9936382890600041 100 $a20241018d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSemi-supervised Tooth Segmentation $eFirst MICCAI Challenge, SemiToothSeg 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings /$fedited by Yaqi Wang, Xiaodiao Chen, Dahong Qian, Fan Ye, Shuai Wang, Hongyuan Zhang 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (X, 194 p. 74 illus., 58 illus. in color.) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v14623 311 08$a9783031723957 311 08$a3031723953 320 $aIncludes bibliographical references and index. 327 $aConvolutional 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. 330 $aThis book constitutes the proceedings of the First MICCAI 2023 Challenge on Semi-supervised Tooth Segmentation, SemiToothSeg 2023, held in Conjunction with MICCAI 2023, in Vancouver, BC, Canada, on October 8, 2023. The 16 full papers presented in this book were carefully reviewed and selected from 64 submissions. The papers were written by participants in the STS challenge to describe their solutions for automatic teeth segmentation using the offcial training dataset released for this purpose. In general, this challenge aims to promote the development of the teeth segmentation in panoramic X-ray images and dental CBCT scans. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v14623 606 $aApplication software 606 $aComputer and Information Systems Applications 615 0$aApplication software. 615 14$aComputer and Information Systems Applications. 676 $a617.607572 702 $aWang$b Yaqi 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910983360203321 996 $aSemi-supervised Tooth Segmentation$94317488 997 $aUNINA