The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge
Open accessAbstract
HighlightsA novel objective evaluation framework for nodule detection algorithms using the largest publicly available LIDC‐IDRI data set.The impact of combining individual systems on the detection performance was investigated.The combination of classical candidate detectors and a combination of deep learning architectures generates excellent results, better than any individual system.Our observer study has shown that CAD detects nodules that were missed by expert readers.We released this set of additional nodules for further development of CAD systems. Graphical abstract Figure. No caption available. ABSTRACT Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC‐IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified. This paper describes the setup of LUNA16 and presents the results of the challenge so far. Moreover, the impact of combining individual systems on the detection performance was also investigated. It was observed that the leading solutions employed convolutional networks and used the provided set of nodule candidates. The combination of these solutions achieved an excellent sensitivity of over 95% at fewer than 1.0 false positives per scan. This highlights the potential of combining algorithms to improve the detection performance. Our observer study with four expert readers has shown that the best system detects nodules that were missed by expert readers who originally annotated the LIDC‐IDRI data. We released this set of additional nodules for further development of CAD systems.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
(2010) · Cited by 14,134
Proceedings of IEEE International Conference on Computer Vision
(1995) · Proceedings of IEEE International Conference on Computer Vision · doi:10.1109/iccv.1995.466933 · Cited by 12,860
Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening
The National Lung Screening Trial Research Team (2011) · New England Journal of Medicine · doi:10.1056/nejmoa1102873 · Cited by 11,064
(1968) · American Association of Industrial Nurses Journal · doi:10.1177/216507996801601208 · Cited by 8,798
Ensemble methods in machine learning
Dietterich, T. G. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-45014-9_1 · Cited by 8,408
Research works printed but not linked: 6
- Cerello, 2010, 3-D object segmentation using ant colonies, Pattern Recognit., 43, 1476, 10.1016/j.patcog.2009.10.007 · doi:10.1016/j.patcog.2009.10.007
- Dou, 2016, Multi-level contextual 3D CNNs for false positive reduction in pulmonary nodule detection, IEEE Trans. Biomed. Eng.
- Efron, 1994, vol. 57
- International Commission on Radiation Units and Measurements, 2008, Receiver operating characteristic analysis in medical imaging, J. ICRU, 8, 1, 10.1093/jicru_ndn003 · doi:10.1093/jicru_ndn003
- Retico, 2009, Pleural nodule identification in low-dose and thin-slice lung computed tomography, Comput. Biol. Med., 39, 1137, 10.1016/j.compbiomed.2009.10.005 · doi:10.1016/j.compbiomed.2009.10.005
- Tieleman, 2012, Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude, COURSERA, 4
Cited by
Ardila, D., Kiraly, A. P., Bharadwaj, S., Choi, B., Reicher, J. J., Peng, L., Tse, D., Etemadi, M., Ye, W., Corrado, G., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0447-x · Cited by 2,167
Recent advances and clinical applications of deep learning in medical image analysis
Chen, X., Wang, X., Zhang, K., Fung, K.-M., Thai, T. C., Moore, K., Mannel, R. S., Liu, H., Zheng, B., & Qiu, Y. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102444 · Cited by 1,080
Yan, K., Wang, X., Lu, L., & Summers, R. M. (2018) · Journal of medical imaging · doi:10.1117/1.jmi.5.3.036501 · Cited by 573
Bandi, P., Geessink, O., Manson, Q., Van Dijk, M., Balkenhol, M., Hermsen, M., Ehteshami Bejnordi, B., Lee, B., Paeng, K., Zhong, A., et al. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2867350 · Cited by 569
Self-citationfastMRI: An Open Dataset and Benchmarks for Accelerated MRI
Žbontar, J., Knöll, F., Sriram, A., Murrell, T., Huang, Z., Muckley, M. J., Defazio, A., Stern, R., Johnson, P., Bruno, M. T., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.08839 · Cited by 528
Metadata
- Authors
- Setio, A. A. A., Traverso, A., de Bel, T., Berens, M. S. N., Bogaard, C. V. D., Cerello, P., Chen, H., Dou, Q., Fantacci, M. E., Geurts, B., Gugten, R. V. D., Heng, P. A., Jansen, B., de Kaste, M. M. J., Kotov, V., Lin, J. Y.-H., Manders, J. T. M. C., Sóñora-Mengana, A., García-Naranjo, J. C., Papavasileiou, E., Prokop, M., Saletta, M., Schaefer-Prokop, C. M., Scholten, E. T., Scholten, L., Snoeren, M. M., Torres, E. L., Vandemeulebroucke, J., Walasek, N., Zuidhof, G. C. A., Ginneken, B. V., & Jacobs, C.
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2017-12
- Volume
- 42
- Pages
- 1-13
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W2584017349
- Cited by
- 1,374
- References
- 63
- FWCI
- 52.97
- Type
- journal article
- Language
- en
- Open access
- bronze
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge. https://app.cochat.ai/papers/10.1016/j.media.2017.06.015
- The paper
- Setio, A. A. A., Traverso, A., de Bel, T., Berens, M. S. N., Bogaard, C. V. D., Cerello, P., Chen, H., Dou, Q., Fantacci, M. E., Geurts, B., Gugten, R. V. D., Heng, P. A., Jansen, B., de Kaste, M. M. J., Kotov, V., Lin, J. Y.-H., Manders, J. T. M. C., Sóñora-Mengana, A., García-Naranjo, J. C., . . . Jacobs, C. (2017). Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge. Medical Image Analysis. https://doi.org/10.1016/j.media.2017.06.015
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
