Summary
Redirect | ||||
---|---|---|---|---|
|
Excerpt |
---|
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. 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 following paper published in Medical Physics is effectively the user's manual for the database and should be cited as follows in all manuscripts that make use of the database:
...
TCIA team strongly encourages users to review pylidc and the Standardized representation of the TCIA LIDC-IDRI annotations using DICOM (DICOM-LIDC-IDRI-Nodules) of the annotations/segmentations included in this dataset before developing custom tools to analyze the XML version.
Localtab Group | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
...
|
...
In addition to following The Cancer Imaging Archive's (TCIA’s) Citation Guidelines, if you use this data in your research, please be sure to include the following attribution in any publications or grant applications along with references to appropriate LIDC publications. A listing of these publications can be found on TCIA's Related Publications page.
LIDC-IDRI Attribution:
The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health, and their critical role in the creation of the free publicly available LIDC/IDRI Database used in this study.
Additional information about using this data, as well as some collection metadata, can be obtained on the Lung Image Database Consortium research page.
...
|
...
|
|
...
Imaging Data
Info |
---|
You can view and download these images on TCIA by logging in and selecting the LIDC-IDRI collection. |
Collection Statistics |
|
---|---|
Modalities | CT |
Number of Patients | 1,010 |
Number of Studies | 1,308 |
Number of Series | 1,018 CT |
Number of Images | 244,527 |
If you are unsure how to download this collection please view Searching by Collection or refer to our TCIA's User's Guide for more detailed instructions on using the site.
Note: On 2012-03-21 the XML associated with patient LIDC-IDRI-0101 was updated with a corrected version of the file. The old version is still available via the wiki if needed for audit purposes.
Metadata
...
|