Tooth Segmentation using U-Net

The important steps of dental image analysis are segmentation of teeth, and this process is essential to achieve proper diagnosis, treatment planning, and orthodontic procedures. Of all the processing tasks involved in dental image processing, tooth segmentation is one of the most vital tasks for effective diagnosis, proper treatment planning, and orthodontic therapy. This paper will use the optimized biomedical image segmentation architecture type of U-Net but specifically for tooth segmentation from dental radiographs or intraoral images. The architecture is essentially encoder-decoder-based, where the decoder rebuilds output segmentation and encodes while trying to hierarchically extract the information inside the images. This structure with skip connection from relevant levels of encoder and decoder retains spatial context but provides high accuracy in segmentation. The dataset of annotated dental images was used to train this model, applying data augmentation for better robustness and generalization. Experimental results demonstrated that the U-Net was better, even in the most challenging cases, such as overlapped structures, poor image quality, and occlusions. It has been validated by other evaluation metrics, DSC and IoU, proving the success of the model compared to other conventional techniques. For dentists, this automated tool becomes a reliable device that reduces human labor and improves the efficiency of diagnosis.