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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.


Data Access

Collection Statistics




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
  • 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" 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


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. 

Localtab Group

titleData Access

Data Access

Click the  Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever . Click the Search button to open our Data Portal, where you can browse the data collection and/or download a subset of its contents.

Data TypeDownload all or Query/Filter
Images (DICOM, 125GB)
DICOM Metadata Digest (CSV)

Radiologist Annotations/Segmentations (XML format)

(Note: see pylidc for assistance using these data)

Nodule Size List (web)
Nodule Counts by Patient (XLS)
Patient Diagnoses (XLS)

Click the Versions tab for more info about data releases.

Third Party Analyses of this Dataset

TCIA encourages the community to publish your analyses of our datasets. Below is a list of such third party analyses published using this Collection:

titleDetailed Description

Detailed Description

Collection Statistics

updated 3/21/2012


CT (computed tomography)
DX (digital radiography) 
CR (computed radiography) 

Number of Participants


Number of Studies


Number of Series


Number of Images


Image Size (GB)124

Reader Annotation and Markup

These links help describe how to use the .XML annotation files which are packaged along with the images in


The Cancer Imaging Archive.  The option to include annotation files in the download is enabled by default, so the XML described here will be included when downloading the LIDC-IDRI images unless you specifically uncheck this option.  If you are only interested in the XML files or you have already downloaded the images you can obtain them here:

The following documentation explains the format and other relevant information about the XML annotation and markup files:


Annotation and Markup Issues/Comments


  1. For a subset of approximately 100 cases from among the initial 399 cases released, inconsistent rating systems were used among the 5 sites with regard to the spiculation and lobulation characteristics of lesions identified as nodules > 3 mm


  1. . The XML nodule characteristics data as it exists for some cases will be impacted by this error. We apologize for any inconvenience.
  2. Also note that the XML files do not store radiologist annotations in a manner that allows for a comparison of individual radiologist reads across cases (i.e., the first reader recorded in the XML file of one CT scan will not necessarily be the same radiologist as the first reader recorded in the XML file of another CT scan).
  3. March 2010: Contrary to previous documentation, the correct ordering for the subjective nodule lobulation and nodule spiculation rating scales stored in the XML files is 1=none to 5=marked. The issue of consistency noted


  1. above still remains to be corrected.

Nodule Size List


  1. On 2012-03-21 the XML associated with patient LIDC-IDRI-0101 was updated with a corrected version of the file.
  2. Per May 2018, Please note that errors exist for two xml files, 044.xml and 191.xml, where one reader recorded one nodule as a "nodule >= 3 mm" but neglected to assign ratings for the nodule characteristics. On June 28, 2018 the files were updated with an explanation at the point of the error in the XML files.
  3. Subject LIDC-IDRI-0396 (139.xml) had an incorrect SOP Instance UID for position 1420. This was fixed on June 28, 2018.
  4. Subject LIDC-IDRI-0510 has an assigned value of 5 for the internalStructure attribute in 187/255.xml. There is no 5th category for internalStructure so this should be considered invalid.

Nodule-Specific Details


Diagnosis Data

For a limited set of cases, LIDC sites were able to identify diagnostic data associated with the case. 

  • tcia-diagnosis-data-2012-04-20.xls
  • Note:  This project has concluded and we are not able to obtain any additional diagnosis data beyond what is available in the above link.

Data was collected for as many cases as possible and is associated at two levels:

  1. Diagnosis at the patient level (diagnosis is associated with the patient)
  2. Diagnosis at the nodule level (where possible)

At each level, data was provided as to whether the nodule was:

  1. Unknown (no data is available)
  2. Benign or non-malignant disease
  3. A malignancy that is a primary lung cancer
  4. A metastatic lesion that is associated with an extra-thoracic primary malignancy

For each lesion, there is also information provided as to how the diagnosis was established including options such as:

  1. unknown - not clear how diagnosis was established
  2. review of radiological images to show 2 years of stable nodule
  3. biopsy
  4. surgical resection
  5. progression or response


You can download this Diagnosis Data at: ^LIDC Diagnosis Data-01-08-10.xls|\

Note: Data is still arriving from the LIDC sites, and so in the coming months this spreadsheet will be updated as more information is received

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.  A related section of the wiki has been dedicated to covering the activities of this project and can be found here: LIDC Conversion to AIM




pylidc  is an  Object-relational mapping  (using  SQLAlchemy ) for the data provided in the  LIDC dataset Some of the capabilities of pylidc  include query of LIDC annotations in SQL-like fashion, conversion of  the nodule segmentation contours into voxel labels, and visualization o f segmentations as image overlays.  If you find this tool useful in your research please cite the following paper:


Matthew C. Hancock, Jerry F. Magnan.  Lung nodule malignancy classification using only radiologist quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database Consortium dataset with two statistical learning methods.  SPIE Journal of Medical Imaging. Dec. 2016.


MAX ("multi-purpose application for XML") performs nodule matching and pmap generation based on the


XML files provided with the LIDC/IDRI Database. It also performs certain QA and QC tasks and other XML-related tasks.

MAX is written in Perl and was developed under RedHat Linux. It has been run under Windows.

Downloading MAX and its associated files implies acceptance of the following notice (also


available here and in the distro as a text file):


Image Added

DISCLAIMER: MAX is not guaranteed to process all input correctly. Possible errors include (but are not limited to) the inability to process correctly some types of nodule ambiguity (where nodule ambiguity refers to overlap between nodule markings having complicated shapes or to overlap between a nodule marking and a non-nodule mark).

Download the distro (max-V107.tgz)


; view/download




 (a text file that is also included in the distro).

LIDC 2 Image Toolbox (Matlab)

This tool is a community contribution developed by Thomas Lampert.  It is designed for extracting individual annotations from the XML files and converting them, and the DICOM images, into TIF format for easier processing in Matlab (LIDC-IDRI dataset).  It is available for download from:

titleCitations & Data Usage Policy

Citations & Data Usage Policy 

Public collection license

titleData Citation

Armato III, S. G., McLennan, G., Bidaut, L., McNitt-Gray, M. F., Meyer, C. R., Reeves, A. P., Zhao, B., Aberle, D. R., Henschke, C. I., Hoffman, E. A., Kazerooni, E. A., MacMahon, H., Van Beek, E. J. R., Yankelevitz, D., Biancardi, A. M., Bland, P. H., Brown, M. S., Engelmann, R. M., Laderach, G. E., Max, D., Pais, R. C. , Qing, D. P. Y. , Roberts, R. Y., Smith, A. R., Starkey, A., Batra, P., Caligiuri, P., Farooqi, A., Gladish, G. W., Jude, C. M., Munden, R. F., Petkovska, I., Quint, L. E., Schwartz, L. H., Sundaram, B., Dodd, L. E., Fenimore, C., Gur, D., Petrick, N., Freymann, J., Kirby, J., Hughes, B., Casteele, A. V., Gupte, S., Sallam, M., Heath, M. D., Kuhn, M. H., Dharaiya, E., Burns, R., Fryd, D. S., Salganicoff, M., Anand, V., Shreter, U., Vastagh, S., Croft, B. Y., Clarke, L. P. (2015). Data From LIDC-IDRI [Data set]. The Cancer Imaging Archive.

titlePublication Citation

Armato SG 3rd, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, Van Beeke EJ, Yankelevitz D, Biancardi AM, Bland PH, Brown MS, Engelmann RM, Laderach GE, Max D, Pais RC, Qing DP, Roberts RY, Smith AR, Starkey A, Batrah P, Caligiuri P, Farooqi A, Gladish GW, Jude CM, Munden RF, Petkovska I, Quint LE, Schwartz LH, Sundaram B, Dodd LE, Fenimore C, Gur D, Petrick N, Freymann J, Kirby J, Hughes B, Casteele AV, Gupte S, Sallamm M, Heath MD, Kuhn MH, Dharaiya E, Burns R, Fryd DS, Salganicoff M, Anand V, Shreter U, Vastagh S, Croft BY.  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. DOI:

titleTCIA Citation

Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 26(6), 1045–1057.

In addition, please be sure to include the following attribution in any publications or grant applications along with references to appropriate LIDC publications:

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.

Other Publications Using This Data

See the LIDC-IDRI section on our Publications page  for other work leveraging this collection.  If you have a publication you'd like to add please  contact the TCIA Helpdesk .


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Version 4 (Current): Updated 2020/09/21

9/21/2020 Maintenance notes: corrected inadvertent inclusion of third-party-generated files in primary-data download manifest

Version 3 (Current): Updated 2015/07/27

Data TypeDownload all or Query/Filter
Images (DICOM, 125GB)*
DICOM Metadata Digest (CSV)
Radiologist Annotations/Segmentations (XML)

Image Added
Nodule Size List (web)
Nodule Counts by Patient (XLS)
Patient Diagnoses (XLS)

*Replace any manifests downloaded prior to 2/24/2020. Please download a new manifest by clicking on the download button in the Images row of the table above. Manifests downloaded prior to 2/24/2020 may not include all series in the collection.

Prior to 7/27/2015, many of the series in the LIDC-IDRI collection, had inconsistent values in the DICOM Frame of Reference UID, DICOM tag (0020,0052).  Each image had a unique value for Frame of Reference (which should be consistent across a series).  This has been corrected.  In addition, the following tags, which were present (but should not have been), were removed: (0020,0200) Synchronization Frame of Reference, (3006,0024) Referenced Frame of Reference, and (3006,00c2) Related Frame of Reference.

Version 2: Updated 2012/03/21

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  if needed for audit purposes.

Version 1: 2011/06/23

There was a "pilot release" of 399 cases of the LIDC CT data via the NCI CBIIT installation of NBIA . The LIDC-IDRI collection contained on TCIA is the complete data set of all 1,010 patients which includes all 399 pilot CT cases plus the additional 611 patient CTs and all 290 corresponding chest x-rays. A table which allows  mapping between the old NBIA IDs and new TCIA IDs  can be downloaded for those who have obtained and analyzed the older data.

  For a subset of approximately 100 cases from among the initial 399 cases released, inconsistent rating systems were used among the 5 sites with regard to the spiculation and lobulation characteristics of lesions identified as nodules > 3 mm. The XML nodule characteristics data as it exists for some cases will be impacted by this error. We apologize for any inconvenience.

  Contrary to previous documentation (prior to March 2010), the correct ordering for the subjective nodule lobulation and nodule spiculation rating scales stored in the XML files is 1=none to 5=marked. The issue of consistency noted above still remains to be corrected.