Computer-aided diagnostics and treatment is one of the fastest-growing areas in the dental field . In this effort, dental X-ray image segmentation plays a crucial role in enabling many dental analyses and interpretations and also enables accurate analysis. Recent advancements in image segmentation have been instrumental in this effort. This paper assesses different segmentation models accuracy on dental X-ray panoramic images. Among these models, Residual UNET with binary cross-entropy achieved the best results. Despite obtaining favorable accuracy, other UNET models exhibited lower Intersection of Union (IOU) values in segmentation masks.