The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans
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Purpose: The development of computer‐aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well‐characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. 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. Methods: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which 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 (“ ,” “ ,” and “non‐ ”). 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. Results: The Database contains 7371 lesions marked “nodule” by at least one radiologist. 2669 of these lesions were marked “ ” by at least one radiologist, of which 928 (34.7%) received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings. Conclusions: The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.
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NoduleMNIST3D is a 3D dataset based on LIDC-IDRI, designed for binary classification of lung nodules by categorizing malignancy level 1/2 as negative and 4/5 as positive, ignoring level 3.
“The NoduleMNIST3D is based on the LIDC-IDRI32, a large public lung nodule dataset, containing images from thoracic CT scans. The dataset is designed for both lung nodule segmentation and 5-level malignancy classification task. To perform binary classification, we categorize cases with malignancy level 1/2 into negative class and 4/5 into positive class, ignoring the cases with malignancy level 3.”
From MedMNIST v2 - A large-scale lightweight benchmark for 2D and 3D biomedical image classification (2023) · cites this paper as [32]
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- R. M. Nishikawa, "Design of a common database for research in mam- mogram image analysis," Proc. SPIE 1905, 548-549 ͑1993͒. · DOI
- R. M. Nishikawa and L. M. Yarusso, "Variations in measured perfor- mance of CAD schemes due to database composition and scoring proto- col," Proc. SPIE 3338, 840-844 ͑1998͒. · DOI
- R. M. Nishikawa et al., "Effect of case selection on the performance of computer-aided detection schemes," Med. Phys. 21, 265-269 ͑1999͒. · DOI
- K. Bowyer et al., in "The digital database for screening mammography," Digital Mammography '96: Proceedings of the Third International Work- shop on Digital Mammography, edited by K. Doi, M. L. Giger, R. M. Nishikawa, and R. A. Schmidt ͑Elsevier, Amsterdam, 1996͒, pp. 431- 434.
- M. Heath et al., in "Current status of the digital database for screening mammography," Digital Mammography '98: Proceedings of the 4th In- ternational Workshop on Digital Mammography, edited by N. Karsse- meijer, M. Thijssen, J. Hendriks, and L. van Erning ͑Kluwer, Dordrecht, 1998͒, pp. 431-434.
- M. Heath, K. Bowyer, D. Kopans, R. Moore, and W. P. Kegelmeyer, in "The digital database for screening mammography," Digital Mammogra- phy 2000: Proceedings of the Fifth International Workshop on Digital Mammography, edited by M. J. Yaffe ͑Medical Physics, Madison, 2000͒, pp. 431-434.
- M. L. Giger, H.-P. Chan, and J. Boone, "Anniversary paper: History and status of CAD and quantitative image analysis: The role of Medical Phys- ics and AAPM," Med. Phys. 35, 5799-5820 ͑2008͒. · DOI · Paper page
- A. P. Reeves et al., "A public image database to support research in computer aided diagnosis," 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009, pp. 3715- 3718.
- S. G. Armato III et al., "The Reference Image Database to Evaluate Response to therapy in lung cancer ͑RIDER͒ project: A resource for the development of change analysis software," Clin. Pharmacol. Ther. 84, 448-456 ͑2008͒. · DOI
- K. W. Clark et al., "Creation of a CT image library for the Lung Screen- ing Study of the National Lung Screening Trial," J. Digit Imaging 20, 23-31 ͑2007͒. · DOI
- D. D. Cody et al., "Normalized CT dose index of the CT scanners used in the National Lung Screening Trial," AJR, Am. J. Roentgenol. 194, 1539- 1546 ͑2010͒. · DOI
- C. A. van Iersel et al., "Risk-based selection from the general population in a screening trial: Selection criteria, recruitment and power for the Dutch-Belgian randomised lung cancer multi-slice CT screening trial ͑NELSON͒," Int. J. Cancer 120, 868-874 ͑2007͒. · DOI
- D. M. Xu et al., "Nodule management protocol of the NELSON ran- domised lung cancer screening trial," Lung Cancer 54, 177-184 ͑2006͒. · DOI
- H. A. Gietema et al., "Pulmonary nodules detected at lung cancer screen- ing: Interobserver variability of semiautomated volume measurements," Radiology 241, 251-257 ͑2006͒. · DOI
- D. M. Xu et al., "Limited value of shape, margin and CT density in the discrimination between benign and malignant screen detected solid pul- monary nodules of the NELSON trial," Eur. J. Radiol. 68, 347-352 ͑2008͒. · DOI
- D. M. Xu et al., "Role of baseline nodule density and changes in density and nodule features in the discrimination between benign and malignant solid indeterminate pulmonary nodules," Eur. J. Radiol. 70, 492-498 ͑2009͒. · DOI
- K. Murphy, B. van Ginneken, A. M. R. Schilham, B. J. de Hoop, H. A. Gietema, and M. Prokop, "A large-scale evaluation of automatic pulmo- nary nodule detection in chest CT using local image features and k-nearest-neighbour classification," Med. Image Anal. 13, 757-770 ͑2009͒. · DOI
- B. van Ginneken et al., "Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study," Med. Image Anal. 14, 707-722 ͑2010͒. · DOI
- E. M. van Rikxoort, B. de Hoop, M. A. Viergever, M. Prokop, and B. van Ginneken, "Automatic lung segmentation from thoracic computed tomog- raphy scans using a hybrid approach with error detection," Med. Phys. 36, 2934-2947 ͑2009͒. · DOI
- T. Doshi, D. Rusinak, R. A. Halvorsen, D. C. Rockey, K. Suzuki, and A. H. Dachman, "CT colonography: False-negative interpretations," Radiol- ogy 244, 165-173 ͑2007͒. · DOI
- L. P. Clarke, B. Y. Croft, E. Staab, H. Baker, and D. C. Sullivan, "Na- tional Cancer Institute initiative: Lung image database resource for imag- ing research," Acad. Radiol. 8, 447-450 ͑2001͒. · DOI
- L. E. Dodd et al., "Assessment methodologies and statistical issues for computer-aided diagnosis of lung nodules in computed tomography: Con- temporary research topics relevant to the Lung Image Database Consor- tium," Acad. Radiol. 11, 462-475 ͑2004͒. · DOI
- S. G. Armato III et al., "Lung Image Database Consortium: Developing a resource for the medical imaging research community," Radiology 232, 739-748 ͑2004͒. · DOI
- M. C. Carrillo, C. A. Sanders, and R. G. Katz, "Maximizing the Alzhe- imer's Disease Neuroimaging Initiative II," Alzheimers and Dementia 5, 271-275 ͑2009͒. · DOI
- M. F. McNitt-Gray et al., "The Lung Image Database Consortium ͑LIDC͒ data collection process for nodule detection and annotation," Acad. Ra- diol. 14, 1464-1474 ͑2007͒. · DOI
- S. G. Armato III et al., "The Lung Image Database Consortium ͑LIDC͒: An evaluation of radiologist variability in the identification of lung nod- ules on CT scans," Acad. Radiol. 14, 1409-1421 ͑2007͒. · DOI
- C. R. Meyer et al., "Evaluation of lung MDCT nodule annotations across radiologists and methods," Acad. Radiol. 13, 1254-1265 ͑2006͒. · DOI
- I. Petkovska et al., "The effect of lung volume on nodule size on CT," Acad. Radiol. 14, 476-485 ͑2007͒. · DOI
- N. Otsu, "A threshold selection method from grey level histograms," IEEE Trans. Syst. Man Cybern. SMC-9, 62-66 ͑1979͒.
- S. G. Armato III et al., "The Lung Image Database Consortium ͑LIDC͒: Ensuring the integrity of expert-defined "truth"," Acad. Radiol. 14, 1455- 1463 ͑2007͒. · DOI
- A. P. Reeves et al., "The Lung Image Database Consortium ͑LIDC͒: A comparison of different size metrics for pulmonary nodule measure- ments," Acad. Radiol. 14, 1475-1485 ͑2007͒. · DOI
- R. Ochs, H. J. Kim, E. Angel, C. Panknin, M. McNitt-Gray, and M. Brown, "Forming a reference standard from LIDC data: Impact of reader agreement on reported CAD performance," Proc. SPIE 6514, 65142A-1- 65142A-6 ͑2007͒. · DOI
- W. F. Sensakovic, A. Starkey, R. Y. Roberts, and S. G. Armato III, "Dis- crete space vs. continuous space lesion boundary and area definition," Med. Phys. 35, 4070-4078 ͑2008͒. · DOI
- A. M. Biancardi, A. C. Jirapatnakul, and A. P. Reeves, "A comparison of ground truth estimation methods," International Journal of Computer As- sisted Radiology and Surgery 5, 295-305 ͑2010͒. · DOI
- W. H. Horsthemke, D. S. Raicu, and J. D. Furst, "Evaluation challenges for bridging semantic gap: Shape disagreements on pulmonary nodules in the Lung Image Database Consortium," International Journal of Health- care Information Systems and Informatics 4, 17-33 ͑2008͒.
- D. Zinovev, D. S. Raicu, J. D. Furst, and S. G. Armato III, "Predicting radiological panel opinions using a panel of machine learning classifiers," J. Algorithms 2, 1473-1502 ͑2009͒. · DOI
- S. G. Armato III et al., "Assessment of radiologist performance in the detection of lung nodules: Dependence on the definition of "truth"," Acad. Radiol. 16, 28-38 ͑2009͒. · DOI
- A. J. Buckler et al., "Volumetric CT in lung cancer: An example for the qualification of imaging as a biomarker," Acad. Radiol. 17, 107-115 ͑2010͒. · DOI
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Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332 · see citing works
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Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018) · Insights into Imaging · doi:10.1007/s13244-018-0639-9 · Cited by 4,682 · see citing works
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Kumar, V., Gu, Y., Basu, S., Berglund, A., Eschrich, S. A., Schabath, M. B., Forster, K., Aerts, H. J. W. L., Dekker, A., Fenstermacher, D., et al. (2012) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2012.06.010 · Cited by 2,258 · see citing works
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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 · see citing works
Harmon, S. A., Sanford, T. H., Xu, S., Turkbey, E. B., Roth, H., Xu, Z., Yang, D., Myronenko, A., Anderson, V., Amalou, A., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17971-2 · Cited by 587 · see citing works
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Majkowska, A., Mittal, S., Steiner, D. F., Reicher, J. J., McKinney, S. M., Duggan, G. E., Eswaran, K., Cameron Chen, P.-H., Liu, Y., Kalidindi, S. R., et al. (2020) · Radiology · doi:10.1148/radiol.2019191293 · Cited by 277 · see citing works
Ciompi, F., de Hoop, B., van Riel, S. J., Chung, K., Scholten, E. T., Oudkerk, M., de Jong, P. A., Prokop, M., & Ginneken, B. V. (2015) · Medical Image Analysis · doi:10.1016/j.media.2015.08.001 · Cited by 289 · see citing works
Yang, D., Xu, Z., Li, W., Myronenko, A., Roth, H. R., Harmon, S., Xu, S., Turkbey, B., Turkbey, E., Wang, X., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101992 · Cited by 279 · see citing works
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Liu, S., Bao, L., Yang, Q., Geng, W., Zheng, B., Li, C., Chen, W., Peng, H., & Yuan, Y. (2026) · arXiv · doi:10.48550/arxiv.2602.03320 · Cited by 1 · see citing works
“…each group to capture the relative quality of the rollouts O i : Land-man et al., 2015; Ji et al., 2022; Ma et al., 2022; Heller et al., 2023; Armato III et al., 2011), MRI (Bernard et al., 2018; Ji et al., 2022; Buda et al., 2019), X-Ray (Jaeger et al., 2014; Chowdhury et al., 2020; Khaled…”
- Authors
- 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., 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. ‐., 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., Vande Casteele, A., 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.
- Affiliations
- University of Chicago (US)
- University of Iowa (US)
- The University of Texas MD Anderson Cancer Center (US)
- University of California, Los Angeles (US)
- University of Michigan (US)
- Cornell University (US)
- Memorial Sloan Kettering Cancer Center (US)
- Weill Cornell Medicine (US)
- Michigan Medicine (US)
- Louisiana State University (US)
- University of Utah (US)
- Beth Israel Deaconess Medical Center (US)
- National Institute of Allergy and Infectious Diseases (US)
- National Institute of Standards and Technology (US)
- University of Pittsburgh (US)
- United States Food and Drug Administration (US)
- Science Applications International Corporation (United States) (US)
- Fujifilm (United States) (US)
- Carestream (United States) (US)
- Philips (Germany) (DE)
- Siemens Healthcare (United States) (US)
- National Cancer Institute (US)
- Corresponding author
- Armato, Samuel G.
- ORCID
- Armato, Samuel G.
- Bidaut, Luc
- McNitt‐Gray, Michael F.
- Meyer, Charles R.
- Reeves, Anthony P.
- Zhao, Binsheng
- Aberle, Denise R.
- Henschke, Claudia I.
- Hoffman, Eric A.
- Kazerooni, Ella A.
- MacMahon, Heber
- Yankelevitz, David
- Biancardi, Alberto M.
- Brown, Matthew S.
- Engelmann, Roger M.
- Smith, Amanda R.
- Gladish, Gregory W.
- Munden, Reginald F.
- Petkovska, Iva
- Quint, Leslie E.
- Sundaram, Baskaran
- Dodd, Lori E.
- Gur, David
- Petrick, Nicholas
- Freymann, John
- Kirby, Justin
- Kuhn, Michael H.
- Dharaiya, Ekta
- Anand, Vikram
- Croft, Barbara Y.
- Clarke, Laurence P.
- Venue
- Medical Physics
- Publisher
- Wiley
- Published
- 2011-01-24
- Volume
- 38
- Issue
- 2
- Pages
- 915-931
- ISSN
- 0094-2405 (ISSN-L)
- 2473-4209
- PMC
- PMC3041807
- OpenAlex
- W1986649315
- Cited by
- 2,887
- References
- 0
- Citations by year
- 2026: 320 · 2025: 350 · 2024: 320 · 2023: 339 · 2022: 319 · 2021: 281 · 2020: 257 · 2019: 237 · 2018: 162 · 2017: 106 · 2016: 78 · 2015: 49 · 2014: 27 · 2013: 27 · 2012: 8
- FWCI
- 18.23
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Medical Physics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Discovery Research Portal (University of Dundee) (publishedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- U.S. Public Health Service (U01CA091085, U01CA091090, U01CA091099, U01CA091100, U01CA091103)
- National Cancer Institute (HHSN261200800001E)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- pulmonary nodules, computed tomography, reference database, computer-aided diagnosis, nodule detection, nodule classification, radiologist annotation, chest CT, public-private partnerships
- MeSH terms
- Diagnosis, Computer-Assisted, Humans, Lung, Lung Neoplasms, Quality Control, Radiographic Image Interpretation, Computer-Assisted, Reference Standards, Radiography, Thoracic, Tomography, X-Ray Computed, Databases, Factual, Tumor Burden
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