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Lung Image Database Consortium: Developing a Resource for the Medical Imaging Research Community
Abstract
To stimulate the advancement of computer-aided diagnostic (CAD) research for lung nodules in thoracic computed tomography (CT), the National Cancer Institute launched a cooperative effort known as the Lung Image Database Consortium (LIDC). The LIDC is composed of five academic institutions from across the United States that are working together to develop an image database that will serve as an international research resource for the development, training, and evaluation of CAD methods in the detection of lung nodules on CT scans. Prior to the collection of CT images and associated patient data, the LIDC has been engaged in a consensus process to identify, address, and resolve a host of challenging technical and clinical issues to provide a solid foundation for a scientifically robust database. These issues include the establishment of (a) a governing mission statement, (b) criteria to determine whether a CT scan is eligible for inclusion in the database, (c) an appropriate definition of the term qualifying nodule, (d) an appropriate definition of "truth" requirements, (e) a process model through which the database will be populated, and (f) a statistical framework to guide the application of assessment methods by users of the database. Through a consensus process in which careful planning and proper consideration of fundamental issues have been emphasized, the LIDC database is expected to provide a powerful resource for the medical imaging research community. This article is intended to share with the community the breadth and depth of these key issues.
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Cited by
Armato, 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., et al. (2011) · Medical Physics · doi:10.1118/1.3528204 · Cited by 2,887
Self-citationComparison and evaluation of methods for liver segmentation from CT datasets
Heimann, T., van Ginneken, B., Styner, M. A., Arzhaeva, Y., Aurich, V., Bauer, C., Beck, A., Becker, C., Beichel, R., Bekes, G., et al. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2013851 · Cited by 1,135
Cheng, J.-Z., Ni, D., Chou, Y.-H., Qin, J., Tiu, C.-M., Chang, Y.-C., Huang, C.-S., Shen, D., & Chen, C.-M. (2016) · Scientific Reports · doi:10.1038/srep24454 · Cited by 755
Lung Nodule Classification Using Deep Features in CT Images
Kumar, D., Wong, A., & Clausi, D. A. (2015) · 2015 12th Conference on Computer and Robot Vision · doi:10.1109/crv.2015.25 · Cited by 398
Computer-aided Diagnosis: How to Move from the Laboratory to the Clinic
van Ginneken, B., Schaefer-Prokop, C. M., & Prokop, M. (2011) · Radiology · doi:10.1148/radiol.11091710 · Cited by 306
Metadata
- Authors
- Armato III, S. G., McLennan, G., McNitt-Gray, M. F., Meyer, C. R., Yankelevitz, D., Aberle, D. R., Henschke, C. I., Hoffman, E. A., Kazerooni, E. A., MacMahon, H., Reeves, A. P., Croft, B. Y., & Clarke, L. P.
- Venue
- Radiology
- Published
- 2004-09
- Volume
- 232
- Issue
- 3
- Pages
- 739-748
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- OpenAlex
- W2136325898
- Cited by
- 380
- References
- 0
- FWCI
- 17.70
- Type
- journal article
- Language
- en
- Open access
- closed
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- Armato III, S. G., McLennan, G., McNitt-Gray, M. F., Meyer, C. R., Yankelevitz, D., Aberle, D. R., Henschke, C. I., Hoffman, E. A., Kazerooni, E. A., MacMahon, H., Reeves, A. P., Croft, B. Y., & Clarke, L. P. (2004). Lung Image Database Consortium: Developing a Resource for the Medical Imaging Research Community. Radiology. https://doi.org/10.1148/radiol.2323032035
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