Tooth Segmentation

Tooth segmentation, the automated identification and delineation of individual teeth in 2D and 3D dental images, aims to improve efficiency and accuracy in dental diagnostics and treatment planning. Current research focuses on developing robust deep learning models, including U-Nets, Transformers, and graph convolutional networks, often incorporating techniques like multi-scale feature fusion, attention mechanisms, and semi-supervised learning to address challenges posed by image noise, blurred boundaries, and limited annotated data. These advancements are significantly impacting dental practice by enabling faster and more precise analysis of dental X-rays and CBCT scans, facilitating improved diagnosis and treatment planning.