Page History

Localtab
active true title Data Access
Data Access Choosing the Download option will provide you with a file to launch the TCIA Download Manager to download the entire collection. If you want to browse or filter the data to select only specific scans/studies please use the Search By Collection option. Tcia head license access Data Type Download all or Query/Filter Images License Images and Radiation Therapy Structures (48.8GB )Image Removed Image Removed ) Tcia button generator url https://wiki.cancerimagingarchive.net/download/attachments/6884551/TCIA_Head-Neck_Cetuximab_06-22-2015.tcia?version=1&modificationDate=1534787422712&api=v2 Tcia button generator label Search url https://www.cancerimagingarchive.net/nbia-search/?CollectionCriteria=Head-Neck%20Cetuximab (Download requires the NBIA Data Retriever) Tcia restricted license DICOM Metadata Digest (CSV, 277 kB) Image Removed Tcia button generator url https://wiki.cancerimagingarchive.net/download/attachments/6884551/HeadNeckCetuximab_MetaData.csv?version=2&modificationDate=1495560309184&api=v2 Tcia cc by 3 Click the Versions tab for more info about data releases.

Supporting Documentation and metadata

Please note that 10 cases in this collection do not contain RT data.  Eight cases whose RT QA scores that were not "Per Protocol" or "Variation Acceptable" were excluded:  96, 133, 141, 143, 154, 182, 475, 478.  Also, subject 243 was ineligible and subject 260 expired prior to follow-up. 

Citations & Data Usage Policy 

This collection is freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License.  See TCIA's Data Usage Policies and Restrictions for additional details. Questions may be directed to help@cancerimagingarchive.net.

Please be sure to include the following citations in your work if you use this data set:

Other Publications Using This Data

TCIA maintains

 a list of publications

 which leverage our

data.

If you have a

manuscript you'd like to add please

 contact TCIA's Helpdesk.

1. AlZu'bi, Shadi et al. "Transferable Hmm Probability Matrices in Multi‐Orientation Geometric Medical Volumes Segmentation." Concurrency and Computation: Practice and Experience, 2019, p. e5214, doi:10.1002/cpe.5214.
2. Edwards, Samuel et al. "Automated 3-D Tissue Segmentation Via Clustering." Journal of Biomedical Engineering and Medical Imaging, vol. 5, no. 2, 2018, p. 08, doi: 10.14738/jbemi.52.4204.

3. Gruselius, H. (2018).  Generative models and feature extraction on patient images and structure data in radiation therapy. Retrieved from  http://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A1215620&dswid=2429

4. Ryalat MH, Laycock S, Fisher M, editors.  Automatic Removal of Mechanical Fixations from CT Imagery with Particle Swarm Optimisation. International Conference on Bioinformatics and Biomedical Engineering; 2017: Springer. DOI:  10.1007/978-3-319-56148-6_37
5. Sando, Yusuke et al. "Real-Time Interactive Holographic 3d Display with a 360 Degrees Horizontal Viewing Zone." Appl Opt, vol. 58, no. 34, 2019, pp. G1-G5, doi:10.1364/AO.58.0000G1.
6. Scarpelli, M. et al. "Optimal Transformations Leading to Normal Distributions of Positron Emission Tomography Standardized Uptake Values." Phys Med Biol, vol. 63, no. 3, 2018, p. 035021, doi:10.1088/1361-6560/aaa175
7. Sinha, A. et al. "The Deformable Most-Likely-Point Paradigm." Med Image Anal, vol. 55, 2019, pp. 148-164, doi:10.1016/j.media.2019.04.013.
8. Sinha, A. et al. "Recovering Physiological Changes in Nasal Anatomy with Confidence Estimates." First International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, edited by Hayit Greenspan et al., Springer, 2019. doi:10.1007/978-3-030-32689-0_12.
9. Tang, Hao et al. "Clinically Applicable Deep Learning Framework for Organs at Risk Delineation in Ct Images." Nature Machine Intelligence, vol. 1, no. 10, 2019, pp. 480-491, doi:10.1038/s42256-019-0099-z.
10. Teske, Hendrik et al. "Handling Images of Patient Postures in Arms up and Arms Down Position Using a Biomechanical Skeleton Model." Current Directions in Biomedical Engineering, vol. 3, no. 2, 2017, pp. 469-472, doi:10.1515/cdbme-2017-0099.
11. Wong, Jordan et al. "Comparing Deep Learning-Based Auto-Segmentation of Organs at Risk and Clinical Target Volumes to Expert Inter-Observer Variability in Radiotherapy Planning." Radiother Oncol, vol. 144, 2019, pp. 152-158, doi:10.1016/j.radonc.2019.10.019.
12. Zhu, Wentao. "Deep Learning for Automated Medical Image Analysis." Computer Science, vol. Ph.D, University of California, Irvine, 15 March 2019 2019. general editor, Xiaohui Xie et al., https://arxiv.org/pdf/1903.04711.pdf.
13. Zhu, Wentao et al. "Anatomynet: Deep Learning for Fast and Fully Automated Whole‐Volume Segmentation of Head and Neck Anatomy." Medical Physics, vol. 46, no. 2, 2018, pp. 576-589, doi:https://doi.org/10.1002/mp.13300

Version 1 (Current): Updated 2013/11/14

Data Type	Download all or Query/Filter
Images (48.8GB)

Image Removed Image Removed

	Tcia button generator url https://wiki.cancerimagingarchive.net/download/attachments/6884551/TCIA_Head-Neck_Cetuximab_06-22-2015.tcia?version=1&modificationDate=1534787422712&api=v2 Tcia button generator label Search url https://www.cancerimagingarchive.net/nbia-search/?CollectionCriteria=Head-Neck%20Cetuximab (Requires the NBIA Data Retriever .)
DICOM Metadata Digest (CSV)