Summary
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<div data-badge-popover="right" data-badge-type='donut' data-doi="10.7937/K9/TCIA.2015.LO9QL9SX" class="altmetric-embed"></div>
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<meta name="citation_title" content=" Data From LIDC-IDRI." /> |
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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. 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.
Excerpt |
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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. 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.
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MAXMAX ("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): 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 ReadMe.txt (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: https://sites.google.com/site/tomalampert/code. Localtab |
Citations & Data Usage PolicyThis collection is freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. See TCIA's Data Usage Policies and Restrictions for additional details. Questions may be directed to help@cancerimagingarchive.net. Please be sure to include the following citations and attributions in your work if you use this data set:
In addition, please be sure to include the following attribution in any publications or grant applications along with references to appropriate LIDC publications:
Other Publications Using This DataSee 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. Localtab |
Version 3 (Current): Updated 2015/07/27
*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 2012/03/21On 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: 2011/06/23There 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. |
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