SummaryThis data set was provided in association with a challenge competition and related conference session conducted at the AAPM 2017 Annual Meeting. The initial winners were announced at the AAPM meeting, but the competition website remains open to others who wish to see how their algorithms perform.
Numerous auto-segmentation methods exist for Organs at Risk in radiotherapy. The overall objective of this auto-segmentation grand challenge is to provide a platform for comparison of various auto-segmentation algorithms when they are used to delineate organs at risk (OARs) from CT images for thoracic patients in radiation treatment planning. The results will provide an indication of the performances achieved by various auto-segmentation algorithms and can be used to guide the selection of these algorithms for clinic use if desirable.
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. Additional download options relevant to the challenge can be found on http://www.autocontouringchallenge.org/ and in the Detailed Description tab.
Click the Versions tab for more info about data releases.
Number of Patients
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Number of Series
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|Image Size (GB)||4.8|
Supporting Documentation and Metadata
To participate in the challenge and to learn more about the subsets of training and test data used please visit www.autocontouringchallenge.org. Some information from the challenge site is included below.
Average 4DCT or free-breathing (FB) CT images from 60 patients, depending on clinical practice, are used for this challenge. Data were acquired from 3 institutions (20 each). Datasets were divided into three groups, stratified per institution:
- 36 training datasets
- 12 off-site test datasets
- 12 live test datasets
Data will be provided in DICOM (both CT and RTSTRUCT), as commonly used in most commercial treatment planning systems.
Contouring Guidelines The manual contours that were used in clinic for treatment planning were used as ground “truth.” All contours were reviewed (and edited if necessary) to ensure consistency across the 60 patients using the RTOG 1106 contouring atlas. Details of contouring guidelines can be found in "Learn the Details". The following organs-at-risk (OARs) are included in this challenge:
- Left and Right Lungs
- Spinal cord
Each training dataset includes a set of DICOM CT image files and one DICOM RTSTRUCT file. Each training dataset is labeled as LCTSC-Train-Sx-yyy, with Sx (x=1,2,3) identifying the institution and yyy identifying the dataset ID in one institution. You may take advantage of this information to optimize your algorithm for testing data acquired from different institutions.
Training data are available here.
Off-site test data
Each off-site test dataset includes a set of DICOM CT image files and is labeled as LCTSC-Test-Sx-10y, with Sx (x=1,2,3) identifying the institution and 10y (y=1,2,3,4) identifying the dataset ID in one instution.
Off-site test data are available here.
Live test data
Each live test dataset includes a set of DICOM CT image files and is labeled as LCTSC-Test-Sx-20y, with Sx (x=1,2,3) identifying the institution and 20y (y=1,2,3,4) identifying the dataset ID in one instution.
Live test data are available here .
Contouring Guidelines from the challenge
Standard name: Esophagus
RTOG Atlas description: The esophagus should be contoured from the beginning at the level just below the cricoid to its entrance to the stomach at GE junction. The esophagus will be contoured using mediastinal window/level on CT to correspond to the mucosal, submucosa, and all muscular layers out to the fatty adventitia.
Additional notes: The superior-most slice of the esophagus is the slice below the first slice where the lamina of the cricoid cartilage is visible (+/- 1 slice). The inferior-most slice of the esophagus is the first slice (+/- 1 slice) where the esophagus and stomach are joined, and at least 10 square cm of stomach cross section is visible.
Standard name: Heart
RTOG Atlas description: The heart will be contoured along with the pericardial sac. The superior aspect (or base) will begin at the level of the inferior aspect of the pulmonary artery passing the midline and extend inferiorly to the apex of the heart.
Additional notes: Inferior vena cava is excluded or partly excluded starting at slice where at least half of the circumference is separated from the right atrium.
Standard names: Lung_L, Lung_R
RTOG Atlas description: Both lungs should be contoured using pulmonary windows. The right and left lungs can be contoured separately, but they should be considered as one structure for lung dosimetry. All inflated and collapsed, fibrotic and emphysematic lungs should be contoured, small vessels extending beyond the hilar regions should be included; however, pre GTV, hilars and trachea/main bronchus should not be included in this structure.
Additional notes: Tumor is excluded in most data, but size and extent of excluded region are not guaranteed. Hilar airways and vessels greater than 5 mm (+/- 2 mm) diameter are excluded. Main bronchi are always excluded, secondary bronchi may be included or excluded. Small vessels near hilum are not guaranteed to be excluded. Collapsed lung may be excluded in some scans. Regions of tumor or collapsed lung that are excluded from training and test data will be masked out during evaluation, such that scores are affected by segmentation choices in those regions.
Standard name: SpinalCord
RTOG Atlas description: The spinal cord will be contoured based on the bony limits of the spinal canal. The spinal cord should be contoured starting at the level just below cricoid (base of skull for apex tumors) and continuing on every CT slice to the bottom of L2. Neuroformanines should not be included.
Additional notes: Spinal cord may be contoured beyond cricoid superiorly, and beyond L2 inferiorly. Contouring to base of skull is not guaranteed for apical tumors.
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 email@example.com.
Please be sure to include the following citations in your work if you use this data set:
Yang, Jinzhong; Sharp, Greg; Veeraraghavan, Harini ; van Elmpt, Wouter ; Dekker, Andre; Lustberg, Tim; Gooding, Mark. (2017). Data from Lung CT Segmentation Challenge. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2017.3r3fvz08
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), Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017. Med. Phys.. . doi:10.1002/mp.13141
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)
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