Description
Book SynopsisToothFairy2: Multi-Structure Segmentation in CBCT Volumes.-
Inferior Alveolar Nerve Segmentation in CBCT Images Using Connectivity-based Selective Re-training.- Scaling nnU-Net for CBCT Segmentation.- DiENTeS: Dynamic ENTity Segmentation with Local-Global Transformers.- Enhanced Multi-Structure Segmentation in CBCT Images with Adaptive Structure Optimization.- Weakly-Supervised Convolutional Neural Networks for Inferior Alveolar Nerve Segmentation in CBCT images.- A Multi-Axial Network for Oral Structural Segmentation.- Automatic Multi-Structure Segmentation in Cone Beam Computed Tomography Volumes Using Deep Encoder-Decoder Architectures.- Video Foundation Model for Medical 3D Segmentation.-
STS: Semi-supervised Teeth Segmentation.-
A Two-Stage Semi-Supervised nnU-Net Model for Automated Tooth Segmentation in Panoramic X-ray Images.- Two-Stage Semi-Supervised nnU-Net Framework for Tooth Segmentation in CBCT Images.- SemiT-SAM: Building a Visual Foundation Model for Tooth Instance Segmentation on Panoramic Radiographs.- Multi-stage Dental Visual Detection Based on YOLOv8: Dental 3D CBCT.- Efficient Semi-Supervised Tooth Instance Segmentation in Panoramic X-rays Using ResUnet50 and SAM Networks.- DAE-Net: Dual Attention Embedding-based Tooth Instance Segmentation Approach for Panoramic X-ray Images.- A Self-Training Pipeline for Semi-Supervised 2D Teeth Instance Segmentation.- Deformable Inherent Consistent Learning Network for Accurate Tooth Segmentation in Dental Panoramic Radiographs.- Semi-Supervised 2D Dental Image Segmentation via Cross Teaching Network.- A Novel Two-Stage Approach for 3D Dental Tooth Instance Segmentation.-
3DTeethLand24: 3D Teeth Landmarks Detection Challenge.-
A Two-Stage Framework with Dual-Branch Network for End-to-End 3D Tooth Landmark Detection.- Leveraging Point Transformers for Detecting Anatomical Landmarks in Digital Dentistry.- ToothInstanceNet: Comprehensive Information from Intra-Oral Scans by Integration of Large-Context and High-Resolution Predictions.
