This repository contains the implementation and dataset related to the paper OralBBNet: Spatially Guided Dental Segmentation of Panoramic X-Rays with Bounding Box Priors.

• 🔥 UFBA-425 Dataset featured in Roboflow100-VL Benchmark for the year of 2025 and referred UFBA-425 as one of the hardest datasets for vision tasks. Find the dataset here and the paper.

• We introduce a set of 425 panoramic X-rays with Human annotated Bounding Boxes and Polygons, the 425 images are a subset of UFBA-UESC Dental Dataset. This dataset can be extensively used for detection and segmentation tasks for Dental Panoramic X-rays. Refer to Description for understanding the organisation of annotations and panoramic X-rays. The Distribution of Categories in the dataset are metnioned in the table below.

• Teeth Numbering Results

• Instance Segmentation Results

• Refer to the paper for further information on model architectures and datasets used for evaluation.

2ddaatagen.ipynb                   => Notebook for generating labels
yolov8_train.ipynb                 => Notebook for training YOLOv8
yolo_test.ipynb                    => Notebook for testing YOLOv8
unet_training.ipynb                => Notebook for training U-Net
unet+cv.ipynb                      => Notebook for training U-Net with cross validation
yolov8+unet_training.ipynb         => Notebook for training OralBBNet
yolov8+unet+cv.ipynb               => Notebook for training OralBBNet with cross validation

Cite the paper if you find our work useful.

@misc{budagam2025oralbbnetspatiallyguideddental,
      title={OralBBNet: Spatially Guided Dental Segmentation of Panoramic X-Rays with Bounding Box Priors}, 
      author={Devichand Budagam and Azamat Zhanatuly Imanbayev and Iskander Rafailovich Akhmetov and Aleksandr Sinitca and Sergey Antonov and Dmitrii Kaplun},
      year={2025},
      eprint={2406.03747},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2406.03747}, 
}