SummaryIn 2004 when presenting the NCI Executive Committee the ACRIN proposal to conduct the National CT Colonography Trial (6664), a case was made that publicly accessible image data sharing would offer a valuable research asset to a wide image processing research community. Adding to the many merits of that proposal, the data-sharing component was strongly endorsed. ACRIN completed the trial expeditiously and its results were published in NEJM in fall 2008 to wide interest. ACRIN has graciously allowed the wider research community access to a portion of the data from that trial here on TCIA, including spreadsheets identifying positive and negative polyp cases. The complete ACRIN 6664 protocol description can be found at http://www.acrin.org/TabID/151/Default.aspx.
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 Type||Download all or Query/Filter|
|Images (DICOM, 462.6GB)|
|DICOM Metadata Digest (CSV)|
|Polyp Descriptions - Large 10mm (XLS)|
|Polyp Descriptions - 6 to 9mm (XLS)|
|Polyp Descriptions - No polyp found (XLS)|
Click the Versions tab for more info about data releases.
Third Party Analyses of this Dataset
TCIA encourages the community to publish your analyses of our datasets. Below is a list of such third party analyses published using this Collection:
Number of Patients
Number of Studies
Number of Series
Number of Images
|Image Size (GB)||462.6|
There are presently 825 cases in this collection with XLS sheets that provide polyp descriptions and their location within the colon segments. To link the XLS polyp tables with the DICOM image studies in TCIA you should understand that some cases in the TCIA are identified by long numbers with the last 4 digits after the last decimal point (e.g.: NCIA study number "126.96.36.199.4.1.9328.50.4.0040" referred to as case "40"). In addition there are a fewer number of additional positive cases that begin their identification number with 'CTC' (e.g.: CTC-5401799343)
Three related XLS spreadsheets are in this release.
- TCIA CTC large 10 mm polyps.xls - Contains the case numbers for 35 cases (out of the 825 total TCIA cases) where at least one 10mm or larger size polyp was found. Individual cases may have several (up to 20) polyps of different sizes listed on a particular XLS row as "LESION 1.x, 2.x,3.x etc. – see "feature key" below).
- TCIA CTC 6 to 9 mm polyps.xls - Contains 69 cases with smaller size polyps.
- TCIA CTC no polyp found.xls - Contains 243 cases that were recorded as free of polyps by both CTC and optical techniques.
Thus in this CT Colonography collection you will be able to download the prone and supine DICOM images from OC same-day validated 243 negative cases, 69 cases with 6 to 9 mm polyps, and 35 cases which have at least one > 10 mm polyp and their histological type. Below is the key for deciphering the features in the spreadsheet.
WARNING: NCI cannot assure archive users of error-free validity of the XL polyp location data since NCI did not itself perform the clinical study or its analysis.
You will note that two XLS files with positive findings have multiple columns descriptors of individual polyp lesions listed as in the table below. The meaning of the colored columns labeled "LESION 1.1...1.2...1.3...1.4, etc" is explained in the attached key-code ".tiff" file entitled "Polyp description key table.tiff"). Some CT scan slice numbers where the polyps were found are provided, but unfortunately the table may not have complete slice number information – you'll just have to do the best you can with the data NCI was given.
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 firstname.lastname@example.org.
Please be sure to include the following citations in your work if you use this data set:
Smith K, Clark K, Bennett W, Nolan T, Kirby J, Wolfsberger M, Moulton J, Vendt B, Freymann J. (2015). Data From CT_COLONOGRAPHY. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2015.NWTESAY1
Johnson, C. D., Chen, M.-H., Toledano, A. Y., Heiken, J. P., Dachman, A., Kuo, M. D., … Limburg, P. J. (2008, September 18). Accuracy of CT Colonography for Detection of Large Adenomas and Cancers. New England Journal of Medicine. New England Journal of Medicine (NEJM/MMS). http://doi.org/10.1056/nejmoa0800996 (paper)
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. (paper)
Other Publications Using This Data
TCIA maintains a list of publications which leverage these datasets.
- Alazmani A, Hood A, et al. Quantitative Assessment of Colorectal Morphology: Implications for Robotic Colonoscopy. Medical Engineering and Physics, 2016. 38(2):148-154. (link)
- Boone DJ, Halligan S, Roth HR, et al., CT Colonography: External Clinical Validation of an Algorithm for Computer-assisted Prone and Supine Registration. Radiology, 2013. 268(3):752-760.(link)
- Gayathri DK, Radhakrishnan R, Rajamani K. Segmentation of colon and removal of opacified fluid for virtual colonoscopy. Pattern Analysis and Applications. 2017:1-15. DOI: 10.1007/s10044-017-0614-y
- Gayathri Devi K, Radhakrishnan R. Automatic Segmentation of Colon in 3D CT Images and Removal of Opacified Fluid Using Cascade Feed Forward Neural Network. Computational and Mathematical Methods in Medicine. 2015;2015.
- Guachi, Lorena et al. "Automatic Colorectal Segmentation with Convolutional Neural Network." Computer-Aided Design and Applications, vol. 16, no. 5, 2019, pp. 836-845, doi:10.14733/cadaps.2019.836-845.
Lin AY, Du P, Dinning PG, Arkwright JW, Kamp JP, Cheng LK, Bissett IP, O'Grady G. High resolution anatomic correlation of cyclic motor patterns in the human colon: Evidence of a rectosigmoid brake. American Journal of Physiology-Gastrointestinal and Liver Physiology. 2017;312(5):G508-G15. DOI: 10.1152/ajpgi.00021.2017.
- Liu, Feng et al. "The Current Role of Image Compression Standards in Medical Imaging." Information, vol. 8, no. 4, 2017, p. 131, doi:10.3390/info8040131
- Namías R, et al., Automatic rectum limit detection by anatomical markers correlation. Computerized Medical Imaging and Graphics, 2014. 38(4):245-250.(link)
- Pang S, Yu Z, Orgun MA. A Novel End-to-End Classifier Using Domain Transferred Deep Convolutional Neural Networks for Biomedical Images. Computer Methods and Programs in Biomedicine. 2017. (link)
- Pang, Shuchao et al. "A Novel Biomedical Image Indexing and Retrieval System Via Deep Preference Learning." Computer Methods and Programs in Biomedicine, vol. 158, 2018, pp. 53-69, doi:10.1016/j.cmpb.2018.02.003.
- Roth HR, et al., External clinical validation of prone and supine CT colonography registration in Abdominal Imaging. Computational and Clinical Applications 2012, Springer. 7601:10-19.(link)
- Yahya-Zoubir B, Hamami L. et al. Automatic 3D Mesh-Based Centerline Extraction from a Tubular Geometry Form. Information Technology and Control, 2016. 45(2):156-163. (link)
If you have a publication you'd like to add please contact the TCIA Helpdesk.
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