Dental Panoramic Radiography Dataset for Pixel level Semantic Segmentation of Teeth
Description
This dataset consists of panoramic dental X ray (OPG) images collected from a clinical imaging facility in Sylhet, Bangladesh. The dataset contains 329 panoramic radiographs obtained from 329 individual patients and is intended for research purposes in dental image analysis and deep learning applications, particularly semantic segmentation of teeth. The data collection was carried out in coordination with the clinical imaging facility, which allowed the collection of panoramic dental radiographs for research purposes on the condition that no patient personal information would be accessed, recorded, or disclosed at any stage of the study. All images were fully anonymized prior to dataset creation to ensure patient confidentiality and ethical compliance. The dataset includes both male and female subjects across different age groups, covering both adult and pediatric cases. It reflects a wide range of dental conditions commonly observed in clinical practice, including complete and incomplete dentition, missing teeth, restorations, impacted teeth, and other developmental variations. This diversity makes the dataset suitable for developing and evaluating robust deep learning models capable of handling real world clinical variability. The majority of samples correspond to adult patients. The images were acquired from panoramic radiographs using smartphone photography under controlled conditions with a high resolution mobile device to preserve anatomical details while maintaining consistency and patient privacy. Although controlled conditions were maintained during acquisition, the dataset still exhibits natural variability in brightness, contrast, sharpness, and overall image quality due to real world clinical imaging environments. All images were manually annotated at the pixel level for semantic segmentation of teeth. The annotation process was performed under the supervision of a professional dental expert to ensure anatomical correctness and clinical reliability. The segmentation masks were carefully reviewed through multiple quality assurance steps to maintain consistency and reduce annotation errors across the dataset. This dataset is intended to support research in dental image analysis, particularly semantic tooth segmentation using deep learning and computer vision techniques. It is also applicable to automated dental assessment, clinical decision support systems, and AI based healthcare applications. The realistic variability and clinical diversity present in the dataset make it suitable for evaluating the robustness and generalization capability of segmentation models under real world conditions.
Files
Institutions
- Leading UniversitySylhet Division, Sylhet