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Summary

Breast cancer is among the most common cancers and a common cause of death among women. Over 39 million breast cancer screening exams are performed every year and are among the most common radiological tests. This creates a high need for accurate image interpretation. Machine learning has shown promise in interpretation of medical images. However, limited data for training and validation remains an issue.

Here, we share a curated dataset of digital breast tomosynthesis images that includes normal, actionable, biopsy-proven benign, and biopsy-proven cancer cases.  The dataset contains four components: (1) DICOM images, (2) a spreadsheet indicating which group each case belongs to (3) annotation boxes, and (4) Image paths for patients/studies/views.  A detailed description of this dataset can be found in the following paper; please reference this paper if you use this dataset:

M. Buda, A. Saha, R. Walsh, S. Ghate, N. Li, A. Święcicki, J. Y. Lo, M. A. Mazurowski, Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model. arXiv preprint arXiv:2011.07995 (https://arxiv.org/abs/2011.07995).

Additional information and resources related to this dataset can be found here: https://sites.duke.edu/mazurowski/resources/digital-breast-tomosynthesis-database/

A Version 1 of the dataset contains only a subset of all data described in the paper above. More data will be share in subsequent versions.


Acknowledgements

We would like to acknowledge the individuals and institutions that have provided data for this collection:

  • Duke University Hospital/Duke University, Durham, NC, USA

  • We would like to acknowledge all those who contributed to the curation of this dataset

  • This work was supported by a grant from the NIH: 1 R01 EB021360 (PI: Mazurowski).


Data Access

Click the  Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever . Click the Search button to open our Data Portal, where you can browse the data collection and/or download a subset of its contents.

Data TypeDownload all or Query/Filter

Training set - Images (DICOM, 1321 GB)

Training set - Image paths for patients/studies/views (csv)
Training set - Spreadsheet indicating which group each cases belongs to (see the paper for details on the groups) (csv)
Training set - Boxes indicating lesion locations, identical to Phase 1 (csv)
Validation set - Images (DICOM, 79 GB)
Validation set - Image paths for patients/studies/views (csv)
Test set - Images (DICOM, 126 GB)
Test set - Image paths for patients/studies/views (csv)

Click the Versions tab for more info about data releases.

Detailed Description

Image Statistics


Modalities

DBT

Number of Participants

5060

Number of Studies

5610

Number of Series

22032

Number of Images

22032

Images Size (GB, compressed)1526

Dataset Split Details

This dataset was originally used for the DBTex2 challenge (https://spie-aapm-nci-dair.westus2.cloudapp.azure.com/competitions/6), which contains a total of 22032 breast tomosynthesis scans from 5060 patients from this collection.  The dataset was broken down into the following cohorts for usage at the competition, a split that is maintained for this general release:

  1. Total: 22032 scans
  2. Training: 19148 scans
  3. Validation: 1163 scans
  4. Test: 1721 scans

Version 1, 2 and 3 of the page had a fraction of each of these subsets released, to accompany phase 1 of the aforementioned competition, DBTex.

Training set (with truth): 

The training set consists of 19148 cases. This dataset will be representative of the technical properties (equipment, acquisition parameters, file format) and the nature of lesions in the validation and test sets. An associated Excel file in CSV format will include DBT scan identifier and the definition of the bounding box of all lesions. 

Validation set (without truth): 

The validation set consists of 1163 cases. The locations of lesions are not provided. The validation set needs to be processed, manipulated, and analyzed without human intervention

Test set (without truth): 

The test set consists of 1721 cases. The locations of lesions are not provided. The test set needs to be processed, manipulated, and analyzed without human intervention. 

Citations & Data Usage Policy

Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution-NonCommercial 4.0 International License under which it has been published. Attribution should include references to the following citations:

Data Citation

Buda, M., Saha, A., Walsh, R., Ghate, S., Li, N., Święcicki, A., Lo, J.Y., Yang, J., & Mazurowski, M.A. (2020). Data from the Breast Cancer Screening – Digital Breast Tomosynthesis (BCS-DBT). Data from The Cancer Imaging Archive. (2020). DOI: https://doi.org/10.7937/e4wt-cd02.

Publication Citation

M. Buda, A. Saha, R. Walsh, S. Ghate, N. Li, A. Święcicki, J. Y. Lo, M. A. Mazurowski, Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model. arXiv preprint https://arxiv.org/abs/2011.07995.

TCIA Citation

Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7

Other Publications Using This Data

TCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.

Version 4 (Current): Updated 2021/06/08

Data TypeDownload all or Query/Filter

Training set - Images (DICOM, 1321 GB)

Training set - Image paths for patients/studies/views (csv)
Training set - Spreadsheet indicating which group each cases belongs to (see the paper for details on the groups) (csv)
Training set Phase 2 - Boxes indicating lesion locations, identical to Phase 1 (csv)
Validation set - Images (DICOM, 79 GB)
Validation set - Image paths for patients/studies/views (csv)
Test set - Images (DICOM, 126 GB)
Test set - Image paths for patients/studies/views (csv)

Added Phase 2 images and spreadsheets. Previous versions (1,2, and 3) are now "Phase 1".

Version 3: Updated 2021/01/15

Data TypeDownload all or Query/Filter
Test set - Images (DICOM, 47 GB)
Test set - Image paths for patients/studies/views (csv)
Validation set - Images (DICOM, 28 GB)
Validation set - Image paths for patients/studies/views (csv)

Training set - Images (DICOM, 186 GB)

Training set - Image paths for patients/studies/views (csv)
Training set - Boxes indicating lesion locations (csv)
Training set - Spreadsheet indicating which group each cases belongs to (see the paper for details on the groups) (csv)

Test set - Images added.

Test set - Image paths for patients/studies/views spreadsheet added.

Version 2: Updated 2021/01/04

Data TypeDownload all or Query/Filter
Validation set - Images (DICOM, 28 GB)
Validation set - Image paths for patients/studies/views (csv)

Training set - Images (DICOM, 186 GB)

Training set - Image paths for patients/studies/views (csv)
Training set - Boxes indicating lesion locations (csv)
Training set - Spreadsheet indicating which group each cases belongs to (see the paper for details on the groups) (csv)

Validation set - Images added.

Validation set - Image paths for patients/studies/views spreadsheet added.

Volume Slices column added to the Training set - Boxes indicating lesion locations spreadsheet.

Version 1: Updated 2020/12/14

Data TypeDownload all or Query/Filter

Training set - Images (DICOM, 186 GB)

Training set - Image paths for patients/studies/views (csv)
Training set - Boxes indicating lesion locations (csv)
Training set - Spreadsheet indicating which group each cases belongs to (see the paper for details on the groups) (csv)
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