Journal article
The Lung Image Database Consortium (LIDC) Data Collection Process for Nodule Detection and Annotation
Academic radiology, Vol.14(12), pp.1464-1474
12/01/2007
DOI: 10.1016/j.acra.2007.07.021
PMCID: PMC2176079
PMID: 18035276
Abstract
The Lung Image Database Consortium (LIDC) is developing a publicly available database of thoracic computed tomography (CT) scans as a medical imaging research resource to promote the development of computer-aided detection or characterization of pulmonary nodules. To obtain the best estimate of the location and spatial extent of lung nodules, expert thoracic radiologists reviewed and annotated each scan. Because a consensus panel approach was neither feasible nor desirable, a unique two-phase, multicenter data collection process was developed to allow multiple radiologists at different centers to asynchronously review and annotate each CT scan. This data collection process was also intended to capture the variability among readers.
Four radiologists reviewed each scan using the following process. In the first or “blinded” phase, each radiologist reviewed the CT scan independently. In the second or “unblinded” review phase, results from all four blinded reviews were compiled and presented to each radiologist for a second review, allowing the radiologists to review their own annotations together with the annotations of the other radiologists. The results of each radiologist’s unblinded review were compiled to form the final unblinded review. An XML-based message system was developed to communicate the results of each reading.
This two-phase data collection process was designed, tested, and implemented across the LIDC. More than 500 CT scans have been read and annotated using this method by four expert readers; these scans either are currently publicly available at
http://ncia.nci.nih.gov or will be in the near future.
A unique data collection process was developed, tested, and implemented that allowed multiple readers at distributed sites to asynchronously review CT scans multiple times. This process captured the opinions of each reader regarding the location and spatial extent of lung nodules.
Details
- Title: Subtitle
- The Lung Image Database Consortium (LIDC) Data Collection Process for Nodule Detection and Annotation
- Creators
- Michael F. McNitt-Gray - David Geffen School of Medicine at UCLASamuel G. Armato - University of ChicagoCharles R. Meyer - University of MichiganAnthony P. Reeves - Cornell UniversityGeoffrey McLennan - University of IowaRichie C. Pais - University of California, Los AngelesJohn Freymann - University of IowaMatthew S. Brown - University of California, Los AngelesRoger M. Engelmann - University of ChicagoPeyton H. Bland - University of MichiganGary E. Laderach - University of MichiganChris Piker - University of IowaJunfeng Guo - University of IowaZaid Towfic - University of IowaDavid P.-Y. Qing - David Geffen School of Medicine at UCLADavid F. Yankelevitz - Cornell UniversityDenise R. Aberle - University of California, Los AngelesEdwin J.R. van Beek - University of IowaHeber MacMahon - University of ChicagoElla A. Kazerooni - University of ChicagoBarbara Y. Croft - National Cancer InstituteLaurence P. Clarke - National Cancer Institute
- Resource Type
- Journal article
- Publication Details
- Academic radiology, Vol.14(12), pp.1464-1474
- DOI
- 10.1016/j.acra.2007.07.021
- PMID
- 18035276
- PMCID
- PMC2176079
- NLM abbreviation
- Acad Radiol
- ISSN
- 1076-6332
- eISSN
- 1878-4046
- Publisher
- Elsevier Inc
- Language
- English
- Date published
- 12/01/2007
- Academic Unit
- Radiology
- Record Identifier
- 9984627252802771
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