Numbering permanent and deciduous teeth via deep instance segmentation in panoramic X-rays

In this repo, you will find the instructions on how to request the data set used to perform the experiments of the aforementioned paper. We manually annotated from scratch a subset of 450 images from the UFBA-UESC Dental Images Deep data set, which comprises 1500 panoramic dental radiographs. We consider that this new data set evolves a previously published data set: DNS Panoramic Images. Therefore, we refer to this new data set as the DNS Panoramic Images v2, where DNS stands for Detection, Numbering, and Segmentation. We presented our results at the 17th International Symposium on Medical Information Processing and Analysis (SIPAIM), and our paper was among the finalists of the best paper award. To be notified of code releases, new data sets, and errata, please watch this repo.

Data set statistics

The data set comprises 450 panoramic images, split into six folds, each containing 75 images. The first five folds were used for cross-validation, while the remaining one constituted the test data set. Therefore, we strongly recommend using fold number 6 (fold-06) as the test data set, so your results can be compared to ours. The annotations are in six JSON files (one for each fold) in the COCO format. We cropped all images to the new 1876×1036 dimensions and converted them to PNG image files. The table below summarizes the data used according to image categories. These categories group the images according to the presence of 32 teeth, restoration, and dental appliances, revealing the high variability of the images. Categories 5 and 6 are reserved for patients with dental implants and more than 32 teeth, respectively. Spoiler: Watch this repo for soon to be published updates.

Citation

If you use this data set, please cite:

L. Pinheiro, B. Silva, B. Sobrinho, F. Lima, P. Cury, L. Oliveira, “Numbering permanent and deciduous teeth via deep instance segmentation in panoramic X-rays,” in Symposium on Medical Information Processing and Analysis (SIPAIM). SPIE, 2021.

@inproceedings{pinheiro2021numbering,
  title={Numbering permanent and deciduous teeth via deep instance segmentation in panoramic X-rays},
  author={Pinheiro, Laís and Silva, Bernardo and Sobrinho, Brenda and Lima, Fernanda and Cury, Patrícia and Oliveira, Luciano.}
  booktitle={Symposium on Medical Information Processing and Analysis (SIPAIM)},
  year={2021},
  organization={SPIE}
}

Previous Works

This data set and its corresponding paper are a continuation of other works of our group. Please, consider reading and citing:

• B. Silva, L. Pinheiro, L. Oliveira, and M. Pithon, “A study on tooth segmentation and numbering using end-to-end deep neural networks,” in Conference on Graphics, Patterns and Images. IEEE, 2020.

@inproceedings{silva2020study,
  title={A study on tooth segmentation and numbering using end-to-end deep neural networks},
  author={Silva, Bernardo and Pinheiro, Laís and Oliveira, Luciano and Pithon, Matheus}
  booktitle={Conference on Graphics, Patterns and Images (SIBGRAPI)},
  year={2020},
  organization={IEEE}
}

• G. Jader, J. Fontineli, M. Ruiz, K. Abdalla, M. Pithon, and L. Oliveira, “Deep instance segmentation of teeth in panoramic X-ray images,” in Conference on Graphics, Patterns and Images. IEEE, 2018.

@inproceedings{jader2018deep,
  title={Deep instance segmentation of teeth in panoramic X-ray images},
  author={Jader, Gil and Fontineli, Jefferson and Ruiz, Marco and Abdalla, Kalyf and Pithon, Matheus and Oliveira, Luciano},
  booktitle={Conference on Graphics, Patterns and Images (SIBGRAPI)},
  pages={400--407},
  year={2018},
  organization={IEEE}
}

• G. Silva, L. Oliveira, and M. Pithon, “Automatic segmenting teeth in X-ray images: Trends, a novel data set, benchmarking and future perspectives,” Expert Systems with Applications, Patterns and Images. vol. 107, pp. 15-31, 2018.

@article{silva2018automatic,
  title={Automatic segmenting teeth in X-ray images: Trends, a novel data set, benchmarking and future perspectives},
  author={Silva, Gil and Oliveira, Luciano and Pithon, Matheus},
  journal={Expert Systems with Applications},
  volume={107},
  pages={15--31},
  year={2018},
  publisher={Elsevier}
}

Demonstration

Follow the provided jupyter notebook (demo.ipynb) to get a quick sense of the data set. The conversions.py file defines useful functions to visualize the annotations.

Request the Data Set

Copy the text below in a PDF file, fill out the fields in the text header, and sign it at the end. Please send an e-mail to [email protected] to receive a link to download the DNS Panoramic Images v2 data set with the PDF in attachment. The e-mail must be sent from a professor’s valid institutional account:

Subject: Request to download the DNS Panoramic Images v2.

“Name: [your first and last name]

Affiliation: [university where you work]

Department: [your department]

Current position: [your job title]

E-mail: [must be the e-mail at the above-mentioned institution]

I have read and agreed to follow the terms and conditions below: The following conditions define the use of the DNS Panoramic Images v2:

This data set is provided “AS IS” without any express or implied warranty. Although every effort has been made to ensure accuracy, IvisionLab does not take any responsibility for errors or omissions;

Without the expressed permission of IvisionLab, any of the following will be considered illegal: redistribution, modification, and commercial usage of this data set in any way or form, either partially or in its entirety;

All images in this data set are only allowed for demonstration in academic publications and presentations;

This data set will only be used for research purposes. I will not make any part of this data set available to a third party. I’ll not sell any part of this data set or make any profit from its use.

[your signature]”

P.S. A link to the data set file will be sent as soon as possible.

GitHub

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