Summary
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| The goal of the CT segmentation challenge was to compare the bias (where possible) and repeatability of automatic, semi-automatic and manual segmentations for lung CT studies. Investigators from Columbia, MGH, Moffitt and Stanford identified |
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| 52 lung CT nodules and made available the data in DICOM format. Algorithm developers and users were requested to submit at least 4 repetitions of their algorithm for each nodule. A variety of image formats for the segmentation volumes were utilized including NIFTI, NRRD, JPG, PNG, DICOM-SEG, DICOM-RT, AIM, and LIDC-XML. The results were ultimately converted into DICOM-SEG format and uploaded back to TCIA. |
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Data Description
Images from multiple TCIA collections were utilized in the challenge and general information about nodule locations were provided as follows:
| Image Collection | Nodule Locations |
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| Lung Phantom (CUMC) | Lung Phantom Nodule Locations |
| QIN Lung and QIN Lung LSC CT (Moffitt - restricted access) | QIN Lung Nodule Locations |
| RIDER Lung CT (MSKCC) | RIDER Lung CT Nodule Locations |
| NSCLC Radiogenomics: Initial Stanford Study of 26 Cases (NSCLC Radiogenomics-Stanford) (Stanford) | NSCLC Radiogenomics Nodule Locations |
| Data from The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans (LIDC-IDRI) (multi-site) | LIDC-IDRI Nodule Locations |
Data Downloads
The subset of image data from each collection that was used in this challenge can be downloaded using the following Shared Lists: 
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Use this Shared List to obtain all images used in the challenge.
Note: Requires access to QIN Lung and QIN Lung LSC restricted collections. Please contact the helpdesk to request permission.
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To download the data please visit the Digital Object Identifier page for this data set at http://dx.doi.org/10.7937/K9/TCIA.2015.1BUVFJR7.