Segmentation of Teeth in Panoramic X-ray Image Using U-net Algorithm

The segmentation of teeth in panoramic X-ray images is a critical task in dental imaging analysis. This study proposes the application of the U-Net algorithm, a convolutional neural network architecture renowned for its prowess in biomedical image segmentation, to identify and delineate teeth structures within these images precisely. The process involves several key stages. Initially, a dataset comprising panoramic X-ray images alongside corresponding annotated masks indicating tooth locations is assembled. The U-Net architecture, featuring an encoder-decoder structure with skip connections, is employed to facilitate robust feature extraction and localization of teeth. Data preprocessing techniques, including normalization and augmentation, are applied to enhance the dataset’s diversity and prepare it for effective model training. The dataset is partitioned into training, validation, and testing subsets, ensuring proper evaluation and validation of the trained model. During training, a suitable loss function, such as the Dice coefficient or cross-entropy loss, is utilized to optimize the U-Net model parameters. Additionally, hyperparameter tuning and rigorous validation processes are conducted to enhance the model’s segmentation accuracy and prevent overfitting. Upon successful training, the model’s performance is evaluated using various metrics like Intersection over Union (IoU), Dice coefficient, and visual inspection of segmented masks overlaid on X-ray images. Finally, the trained U-Net model is deployed for inference on new panoramic X-ray images, providing precise segmentation of teeth structures. This proposed methodology leverages the robustness and accuracy of the U-Net algorithm, offering a promising approach for automated and accurate teeth segmentation in panoramic X-ray images, thereby assisting dental professionals in diagnosis and treatment planning. In addition to this, we aim to improve the accuracy and efficiency of dental diagnosis, treatment planning, and research by automatically identifying and delineating the boundaries of teeth in X-ray images.