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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 50 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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| Shared List Name | Content Type | File Format | Notes |
|---|---|---|---|
| QIN Lung CT Challenge | Images | DICOM (CT) | Use this Shared List to obtain all images used in the challenge. |
| QIN Lung CT Challenge Segmentations | Segmentations | DICOM (SEG) | Use this Shared List to download all segmentations associated with the challenge |
| QIN Lung CT Segmentation Challenge Images and Results | Images + coming soon) Segmentations | DICOM (CT, SEG) | Use this Shared List to download all images and segmentations associated with the challenge |
| N/A - hosted externally on NCIP Hub (coming soon) | Images + Segmentations | NIFTI | Use this link to download all images and segmentation files in NIFTI format via NCIP Hub. Note: Requires NCIP Hub account and permission to the project. |
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