The Lung Image Database Consortium image collection (LIDC-IDRI) consists of diagnostic and lung cancer screening thoracic computed tomography (CT) scans with marked-up annotated lesions. It is a web-accessible international resource for development, training, and evaluation of computer-assisted diagnostic (CAD) methods for lung cancer detection and diagnosis.
Armato SG III, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, van Beek EJR, Yankelevitz D, et al.: The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans. Medical Physics, 38: 915--931, 2011.
Important note: There was a pilot release of 399 cases of the LIDC CT data via the NCI CBIIT installation of NBIA. This is the complete data set of all 1,010 patients which includes all pilot CT cases as well as the additional patients and all corresponding chest x-rays.
Additional information about using this data as well as some collection meta data can be obtained in the Supporting Documentation below.
CT, DX, CR
Number of Patients
Number of Studies
Number of Series
Number of Images
You can view and download these images on the Cancer Imaging Archive. You will need an user account to log in. Simply follow these steps:
- Navigate to https://cancerimagingarchive.net
- Request a user account if you don't already have one.
- Click the "Search Images" link in the center of the page
- Scroll down through the search criteria until you see the "Collections" section
- Select the "LIDC-IDRI" check box
- Press "Submit"
This will return the full list of cases included in the collection. To download the associated DICOM images:
- Press the "Check All" button and then "Add to Basket"
- Press the "View My Basket" button at the bottom of the page (or "View Contents" in the left menu bar)
- Press the "Download Manager" button to open a Java applet and specify where you'd like to save your images
More information about the Cancer Imaging Program's Program Announcement for LIDC can be found at: http://imaging.cancer.gov/programsandresources/InformationSystems/LIDC
Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process.
Seven academic centers and eight medical imaging companies collaborated to create this data set which contains 1018 cases. Each subject includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ("nodule > or =3 mm," "nodule <3 mm," and "non-nodule > or =3 mm"). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus.
Note : The TCIA team strongly encourages users to review pylidc and the DICOM representation of the annotations/segmentations included in this dataset before developing custom tools to analyze the XML version.
Nodule Size List
You can download this Diagnosis Data at: LIDC Diagnosis Data-01-08-10.xls
Note: This data has not yet been updated to match the new patient ID structure
AIM Annotation Conversion Project
As part of an effort to move towards standard formats for annotation and markup a project has been undertaken to convert this data from the LIDC project into Annotated Image Markup format (AIM). AIM is a standard which was developed out of the caBIG program. More information about this effort can be found here on the NCI CBIIT wiki: LIDC Conversion to AIM