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.
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 . Altmetrics
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