LCTSC PyDicer Test Data
<p>Subset of TCIA LCTSC data which has been converted using PyDicer for use in testing and examples.</p><p><strong>Warning:</strong> This data has been manipulated (downsampled) to reduce size to make it more appropriate for testing.</p><p>Citations & Data Usage Policy </p><p>Users of this data must abide by the <a href="https://wiki.cancerimagingarchive.net/x/c4hF">TCIA Data Usage Policy</a> and the <a href="https://creativecommons.org/licenses/by/3.0/">Creative Commons Attribution 3.0 Unported License</a> under which it has been published. Attribution should include references to the following citations:</p><p>Data Citation</p><p>Yang, J., Sharp, G., Veeraraghavan, H., Van Elmpt, W., Dekker, A., Lustberg, T., & Gooding, M. (2017). <strong>Data from Lung CT Segmentation Challenge (LCTSC) (Version 3) [Data set]</strong>. The Cancer Imaging Archive. <a href="https://doi.org/10.7937/K9/TCIA.2017.3R3FVZ08">https://doi.org/10.7937/K9/TCIA.2017.3R3FVZ08</a></p><p>Publication Citation</p><p>Yang, J. , Veeraraghavan, H. , Armato, S. G., Farahani, K. , Kirby, J. S., Kalpathy‐Kramer, J. , van Elmpt, W. , Dekker, A. , Han, X. , Feng, X. , Aljabar, P. , Oliveira, B. , van der Heyden, B. , Zamdborg, L. , Lam, D. , Gooding, M. and Sharp, G. C. (2018), <strong>Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017</strong>. Med. Phys. <a href="https://doi.org/10.1002/mp.13141">https://doi.org/10.1002/mp.13141</a> </p><p>TCIA Citation</p><p>Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). <strong>The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository.</strong> In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC. <a href="https://doi.org/10.1007/s10278-013-9622-7">https://doi.org/10.1007/s10278-013-9622-7</a></p>
ShareScore
32/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 4
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 8
- Engagement
- 0