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A survey on lung CT datasets and research trends

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Abstract

Purpose

Lung cancer is the most dangerous of all forms of cancer and it has the highest occurrence rate, world over. Early detection of lung cancer is a difficult task. Medical images generated by computer tomography (CT) are being used extensively for lung cancer analysis and research. However, it is essential to have a well-organized image database in order to design a reliable computer-aided diagnosis (CAD) tool. Identifying the most appropriate dataset for the research is another big challenge.

Literarture review

The objective of this paper is to present a review of literature related to lung CT datasets. The Cancer Imaging Archive (TCIA) consortium collates different types of cancer datasets and permits public access through an integrated search engine. This survey summarizes the research work done using lung CT datasets maintained by TCIA. The motivation to present this survey was to help the research community in selecting the right lung dataset and to provide a comprehensive summary of the research developments in the field.

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References

  • Abbas Q. Nodular-deep: classification of pulmonary nodules using deep neural network. Int. J.Med. Res. Heal. Sci. 2017.

  • Aerts HJWL, et al. Decoding tumor phenotype by non-invasive imaging using a quantitative radiomics approach. Nat. Commun. 2014;5.

  • Akram S, Javed MY, Akram MU, Qamar U, Hassan A. Pulmonary nodules detection and classification using hybrid features from computerized tomographic images. J Med Imaging Heal Informatics. 2016.

  • Akram S, Javed MY, Qamar U, Khanum A, Hassan A Artificial neural network-based classification of lungs nodule using hybrid features from computerized tomographic images. Appl Math Inf Sci , 2015.

  • Anitha SAAJ, Peter JD. Automatic lung segmentation in low-dose chest CT scans using convolutional deep and wide network (CDWN). Neural Comput & Applic. 2018;32:15845–55. https://doi.org/10.1007/s00521-018-3877-3.

  • Armato S G et al., "The Lung Image Database Consortium" LIDC … and Image Database Resource Initiative" IDRI … : A completed reference database of lung nodules on CT scans," no. February, pp. 915–931, 2011.

  • Armato SG, et al. LUNGx challenge for computerized lung nodule classification. J Med Imaging. 2016;3(4):044506.

    Article  Google Scholar 

  • Berbaum KS, Franken EA, Dorfman DD, Rooholamini SA, Kathol H, Barloon TJ, et al. Satisfaction of search in diagnostic radiology. Investig. Radiol. 1990.

  • Bhandary, A., Prabhu, G.A., Rajinikanth, V., Thanaraj, K.P., Satapathy, S.C., Robbins, D.E., Shasky, C., Zhang, Y.D., Tavares, JMRS, Raja, N.S.M.: Deep-learning framework to detect lung abnormality – a study with chest X-ray and lung CT scan images. Pattern Recognit. Lett., 2020.

  • Golosio B, Masala GL, Piccioli A, Oliva P, Carpinelli M, Cataldo R, et al. A novel multi-threshold method for nodule detection in lung CT. Med Phys. 2009;36:3607–18.

    Article  Google Scholar 

  • Cai W, Chen S. Zhang D. Pattern Recognit: Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation; 2007.

    Google Scholar 

  • Cascio D, Magro R, Fauci F, Iacomi M, Raso G. Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models. Comput Biol Med. 2012;42:1098–109. https://doi.org/10.1016/j.compbiomed.2012.09.002.

    Article  Google Scholar 

  • Causey JL, Zhang J, Ma S, Jiang B, Qualls JA, Politte DG, et al. A highly accurate model for prediction of lung nodule malignancy with CT scans. Sci Rep. 2018;8:9286. https://doi.org/10.1038/s41598-018-27569-w.

    Article  Google Scholar 

  • Chaddad A, Desrosiers C, Toews M, Abdulkarim B. Predicting survival time of lung cancer patients using radiomic analysis. Oncotarget. 2017;8. https://doi.org/10.18632/oncotarget.22251.

  • Chang C-C, Lin C-J. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol. 2011;2:1–27.

    Article  Google Scholar 

  • Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, et al. The cancer imaging archive (TCIA): maintaining and operating a public information repository. J Digit Imaging. 2013;26(6):1045–57.

    Article  Google Scholar 

  • Clarke LP, Croft BY, Staab E, Baker H, Sullivan DC. National Cancer Institute initiative: lung image database resource for imaging research. Acad. Radiol. 2001;8:447–50.

    Article  Google Scholar 

  • Cui G, Wu L, Zhou T, Gu Y, Lu X, Zhang B, et al. Automatic lung nodule detection using multi-scale dot nodule-enhancement filter and weighted support vector machines in chest computed tomography. PLoS One. 2019;14:1–25. https://doi.org/10.1371/journal.pone.0210551.

    Article  Google Scholar 

  • De Carvalho Filho AO, De Sampaio WB, Silva AC, de Paiva AC, Nunes RA, Gattass M. Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index. Artif Intell Med. 2014;60:165–77. https://doi.org/10.1016/j.artmed.2013.11.002.

    Article  Google Scholar 

  • Dilger SK, et al. Improved pulmonary nodule classification utilizing quantitative lung parenchyma features. Journal of Medical Imaging. 2015;2:041004.

    Article  Google Scholar 

  • Dodd LE, Wagner RF, Armato SG 3rd, McNitt-Gray M, Beiden S, Chan HP, et al. Assessment methodologies and statistical issues for computer-aided diagnosis of lung nodules in computed tomography. Acad Radiol. 2004;11:462–75.

  • El-Regaily SA, Salem MAM, Aziz MHA, Roushdy MI. Lung nodule segmentation and detection in computed tomography. In: 2017 Eighth international conference on intelligent computing and information systems (ICICIS): IEEE; 2017.

  • Farahani FV, Ahmadi A, Hossein M, Zarandi F. Hybrid intelligent approach for diagnosis of the lung nodule from CT images using spatial kernelized fuzzy c-means and ensemble learning. Math Comput Simul. 2018;149:48–68. https://doi.org/10.1016/j.matcom.2018.02.001.

    Article  MathSciNet  MATH  Google Scholar 

  • Forouzanfar, et al. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388:1659–724. https://doi.org/10.1016/S0140-6736(16)31679-8.

    Article  Google Scholar 

  • Ferlay J, Ervik M, Lam F, Colombet M, Mery L, Piñeros M, et al. Global Cancer Observatory: cancer today. International Agency for Research on Cancer: Lyon, France; 2018.

    Google Scholar 

  • Grove, O., Berglund, A.E., Schabath, M.B., Aerts, HJWL: quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. 2, 1–14, PLoS One, 2015. https://doi.org/10.1371/journal.pone.0118261.

  • Gu Y, Kumar V, Hall LO, Goldgof DB, Li CY, Korn R, et al. Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach. Pattern Recogn. Mar. 2013;46(3):692–702.

    Article  Google Scholar 

  • Hancock MC, Magnan JF. Predictive capabilities of statistical learning methods for lung nodule malignancy classification using diagnostic image features: an investigation using the lung image database consortium dataset. In: Medical imaging 2017: computer-aided diagnosis, vol 10134. International Society for Optics and Photonics; 2017.

    Google Scholar 

  • Hawkins SH, Korecki JN, Balagurunathan Y, Gu Y, Kumar V, Basu S, et al. Predicting outcomes of non-small cell lung cancer using CT image features. IEEE Access. 2014;2:1418–26. https://doi.org/10.1109/ACCESS.2014.2373335.

    Article  Google Scholar 

  • Henschke CI, McCauley DI, Yankelevitz DF, Naidich DP, McGuinness G, Miettinen OS, et al. Early lung cancer action project: overall design and findings from baseline screening. Lancet. 1999;354:99–105.

    Article  Google Scholar 

  • Huang, X., Sun, W., Tseng, T.L. (Bill), Li, C., Qian, W.: Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks. Comput. Med. Imaging Graph. 74, 2019a.

  • Huang L, Chen J, Hu W, Xu X, Liu D, Wen J, et al. Assessment of a radiomic signature developed in a general NSCLC cohort for predicting overall survival of ALK-positive patients with different treatment types. Clin Lung Cancer. 2019b;20:e638–51. https://doi.org/10.1016/j.cllc.2019.05.005.

    Article  Google Scholar 

  • Huidrom R, Chanu YJ, Singh KM. Pulmonary nodule detection on computed tomography using the neuro-evolutionary scheme. Signal, Image Video Process. 2019;13. https://doi.org/10.1007/s11760-018-1327-4.

  • Jaffar MA, Zia MS, Hussain M, Siddiqui AB, Akram S, Jamil U. An ensemble shape gradient features descriptor based nodule detection paradigm: a novel model to augment complex diagnostic decisions assistance. Multimed Tools Appl. 2018;27.

  • Kang G, Liu K, Hou B, Zhang N. 3D multi-view convolutional neural networks for lung nodule classification. PLoS One. 2017;12:e0188290. https://doi.org/10.1371/journal.pone.0188290.

    Article  Google Scholar 

  • Kannan SR, Ramathilagam S, Devi R, Sathya A. Robust kernel FCM in segmentation of breast medical images. Expert Syst. Appl. 2011;38.

  • Kannan SR, Ramathilagam S, Sathya A, Pandiyarajan R. Effective fuzzy c-means based kernel function in segmenting medical images. Comput. Biol. Med. 2010;40.

  • Kavitha, M.S., Shanthini, J., Sabitha, R.: ECM-CSD : an efficient classification model for cancer stage diagnosis in CT lung images using FCM and SVM techniques, 2019.

    Google Scholar 

  • Kaya A, Can A. A weighted rule-based method for predicting malignancy of pulmonary nodules by nodule characteristics. J Biomed Inform. 2015;56:69–79.

    Article  Google Scholar 

  • Khan SA, Kenza K, Nazir M, Usman M. Proficient lungs nodule detection and classification using machine learning techniques. J Intell Fuzzy Syst. 2015;28:905–17. https://doi.org/10.3233/IFS-141372.

    Article  Google Scholar 

  • Kingma DP, Adam Ba J: a method for stochastic optimization. CoRR. arXiv:1412.6980, 2014.

  • Krizhevsky A, Sutskever I, Hinton GE ImageNet classification with deep convolutional neural networks. In: NIPS 2012.

  • LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86:2278–324. https://doi.org/10.1109/5.726791.

    Article  Google Scholar 

  • Lee Y, Hara T, Fujita H, Itoh S, Ishigaki T. Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique. IEEE Trans Med Imaging. 2001;20.

  • Li W, Cao P, Zhao D, Wang J. Pulmonary nodule classification with deep convolutional neural networks on computed tomography images. Comput Math Methods Med. 2016;2016:1–7. https://doi.org/10.1155/2016/6215085.

    Article  Google Scholar 

  • Li Q, Li F, Doi K. Computerized detection of lung nodules in thin-section CT images by use of selective enhancement filters and an automated, rule-based classifier. Acad Radiol. 2008;15:165–75. https://doi.org/10.1016/j.acra.2007.09.018.

    Article  Google Scholar 

  • Li Q, Sone S, Doi K. Selective enhancement filters for nodules, vessels, and airway walls in two-and three-dimensional CT scans. Med Phys. 2003;30:2040–51. https://doi.org/10.1118/1.1581411.

    Article  Google Scholar 

  • Li W, Nie SD, Cheng JJ. A fast, automatic method of lung segmentation in CT images using mathematical morphology. World Congress Med Phys Biomed Eng. 2006;14.

  • Liu K. Kang G. International Journal of Imaging Systems and Technology: Multiview convolutional neural networks for lung nodule classification; 2017.

    Google Scholar 

  • Lückehe D, von Voigt G. Evolutionary image simplification for lung nodule classification with convolutional neural networks. Int J Comput Assist Radiol Surg. 2018;13:1499–513. https://doi.org/10.1007/s11548-018-1794-7.

    Article  Google Scholar 

  • Lung CT-Diagnosis. 2012. https://wiki.cancerimagingarchive.net/display/Public/LungCT-Diagnosis.

  • McNitt-Gray MF, et al. The Lung Image Database Consortium (LIDC) data collection process for nodule detection and annotation. Acad Radiol. 2007.

  • Meraj T, et al. Lung nodules detection using semantic segmentation and classification with optimal features. Neural Comput. Appl. 2020;2.

  • Messay T, Hardie RC, Rogers SK. A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Med. Image Anal. 2010a;14(3).

  • Messay T, et al. A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Med. Image Anal. 2010b;14.

  • NLST, National Lung Screening Trial (NLST), 2013. https://wiki.cancerimagingarchive.net/display/NLST.

  • Nibali A, He Z, Wollersheim D. Lung nodules diagnosis based on an evolutionary convolutional neural network. Int. J. Comput. Assist. Radiol. Surg. 2017;12. https://doi.org/10.1007/s11548-017-1605-6.

  • Ninomiya K, Arimura H. Homological radiomics analysis for prognostic prediction in lung cancer patients. Phys Medica. 2020;69:90–100. https://doi.org/10.1016/j.ejmp.2019.11.026.

    Article  Google Scholar 

  • Noel R. Wardwell Jr, Pierre P. Massion. Novel strategies for the early detection and prevention of lung cancer. National Center for Biotechnology Information, 2005.

  • NSCLC-Radiomics, Non-small cell lung cancer – Radiomics, 2014. https://doi.org/10.7937/K9/TCIA.2015.PF0M9REI

  • Ou SH, Bartlett CH, Mino-Kenudson M, Cui J, Iafrate AJ. Crizotinib for the treatment of ALK-rearranged nonsmall-cell lung cancer: a success story to usher in the second decade of molecular targeted therapy in oncology. Oncologist. 2012;17:1351–75.

    Article  Google Scholar 

  • Paul R, Hawkins SH, Balagurunathan Y, Schabath MB, Gillies RJ, Hall LO, et al. Deep feature transfer learning in combination with traditional features predicts survival among patients with lung adenocarcinoma. Tomography. 2016a;2. https://doi.org/10.18383/j.tom.2016.00211.

  • Paul R, Hawkins SH, Hall LO, Goldgof DB, Gillies RJ. Combining deep neural network and traditional image features to improve survival prediction accuracy for lung cancer patients from diagnostic CT. IEEE International Conference on Systems, Man, and Cybernetics. 2016b.

  • Pu J, J. Roos, A. Y. Chin, S. Napel, G. D. Rubin, D. S. Paik, Adaptive border marching algorithm: automatic lung segmentation on chest ct images, Computerized Medical Imaging and Graphics ,2008.

  • Qi C. Improved two-dimensional Otsu image segmentation method, and fast recursive realization. Technol: J. Electron. Inf; 2010.

    Google Scholar 

  • Quinlan J R, "Decision trees and decision-making," IEEE Trans. Syst., Man, Cybern., vol. 20, no. 2, pp. 339–346, Mar./Apr. 1990.

  • Rego J, Tan K. Advances in imaging-the changing environment for the imaging specialist. Perm J. 2006;10:26–8.

    Article  Google Scholar 

  • Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, LeCun Y, Over feat: integrated recognition, localization, and detection using convolutional networks. In: Proceedings of ICLR 2014.

  • Setio AAA, Ciompi F, Litjens G, Gerke P, Jacobs C, Sv Riel, Wille MW, Naqibullah M, Sanchez C, Bv Ginneken Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans Med Imaging, 2016, 35, 1160, 1169.

  • Shaffie , A. Soliman, L. Fraiwan, M. Ghazal, F. Taher, N. Dunlap, B. Wang, V. van Berkel, R. Keynton, A. Elmaghraby, et al., A generalized deep learning-based diagnostic system for early diagnosis of various types of pulmonary nodules. Technol. Cancer Res. Treat 2018.

  • Shanthi T, R.S. Sabeenian, Modified Alexnet architecture for classification of diabetic retinopathy images. Comput Electr. Eng 76 ,2019.

  • Shaukat F, Raja G, Gooya A, Frangi AF. Fully automatic detection of lung nodules in CT images using a hybrid feature set: med. Phys. 2017;44:3615–29. https://doi.org/10.1002/mp.12273.

    Article  Google Scholar 

  • Shaukat F, Raja G, Ashraf R, Khalid S, Ahmad M, Ali A. Artificial neural network-based classification of lung nodules in CT images using intensity, shape and texture features. J Ambient Intell Human Comput. 2019;10:4135–49.

    Article  Google Scholar 

  • Shayesteh SP, Shiri I, Karami AH, Hashemian R, Kooranifar S, Ghaznavi H, et al. Predicting lung cancer patients’ survival time via logistic regression-based models in a quantitative radiomic framework. J. Biomed. Phys. Eng. 2019. https://doi.org/10.31661/jbpe.v0i0.1027.

  • Shen S, Bui AA, Cong J, Hsu W. An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy. Computers in biology and medicine. 2015;57.

  • Shen W, et al. Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recognition. 2017;61.

  • Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA. Cancer J. Clin. 2020;70(1).

  • Singadkar G, Mahajan A, Thakur M, Talbar S. Automatic lung segmentation for the inclusion of juxtapleural nodules and pulmonary vessels using curvature based border correction. J King Saud Univ - Comput Inf Sci. 2018. https://doi.org/10.1016/j.jksuci.2018.07.005.

  • Sivakumar S. Chandrasekar C. International Journal of Engineering and Technology (IJET): Lung nodule detection using fuzzy clustering and support vector machines; 2013.

    Google Scholar 

  • Soh L K, C. Tsatsoulis, Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices, IEEE Transactions on Geoscience & Remote Sensing, 37 ,1999.

  • Sonka M, Hlavac V, Boyle R. Image processing, analysis, and machine vision. Cengage Learning. 2014.

  • Song Q, Zhao L, Luo X, Dou X. Using deep learning for classification of lung nodules on computed tomography images. Journal of healthcare engineering. 2017.

  • SPIE-AAPM Lung CT Challenge,2014. https://doi.org/10.7937/K9/TCIA.2015.UZLSU3FL.

  • Sun W, Jiang M, Dang J, Chang P, Yin FF. Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis. Radiat Oncol. 2018;13:197. https://doi.org/10.1186/s13014.018.1140.9.

  • TCIA, The Cancer imaging archive. https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI.

  • The Lung Image Database Consortium image collection (LIDC-IDRI). https://doi.org/10.1186/1745-6215-8-16.

  • Tierney JF, Stewart LA, Ghersi D, et al. Practical methods for incorporating summary time-to-event data into meta-analysis. Trials. 2007;8:16. https://doi.org/10.1186/1745-6215-8-16.

  • Tran GS, Nghiem TP, Nguyen VT, Luong CM, Burie JC. Improving the accuracy of lung nodule classification using deep learning with focal loss. J Healthc Eng. 2019;2019:1–9. https://doi.org/10.1155/2019/5156416.

    Article  Google Scholar 

  • Valente IRS, Cortez PC, Neto EC, Soares JM, de Albuquerque VHC, Tavares JMRS. Automatic 3D pulmonary nodule detection in CT images: a survey. Comput Methods Prog Biomed. 2015.

  • Velazquez ER, Parmar C, Jermoumi M, Mak RH, Van Baardwijk A, Fennessy FM, et al. Volumetric CT-based segmentation of NSCLC using 3D-slicer. Sci Rep. 2013;3.

  • Wang J, et al. Prediction of malignant and benign of lung tumor using a quantitative radiomic method. EMBC. 2016.

  • Xiao X, Zhao J, Qiang Y, Wang H, Xiao Y, Zhang X, et al. An automated segmentation method for lung parenchyma image sequences based on fractal geometry and convex hull algorithm. Appl Sci. 2018;8. https://doi.org/10.3390/app8050832.

  • Xie Y, Zhang J, Xia Y, Fulham M, Zhang Y. Fusing texture, shape, and deep model-learned information at decision level for automated classification of lung nodules on chest CT. Inf Fusion. 2017;42:102–10. https://doi.org/10.1016/j.inffus.2017.10.005.

    Article  Google Scholar 

  • Xiuhua G, Tao S, Zhigang L. Prediction Models for Malignant Pulmonary Nodules Based-on Texture Features of CT Image. In: Prediction models for malignant pulmonary nodules based-on texture features of CT image, In Theory and Applications of CT Imaging and Analysis; 2011. https://doi.org/10.5772/14766.

    Chapter  Google Scholar 

  • Yang L, Yang J, Zhou X, Huang L, Zhao W, Wang T, et al. Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients. Eur Radiol. 2019;29:2196–206. https://doi.org/10.1007/s00330-018-5770-y.

    Article  Google Scholar 

  • Zhang G, et al. An appraisal of nodule diagnosis for lung cancer in CT Images. J. Med. Syst. 2019;43(7).

  • Zhang F, Song Y, Cai W, Lee M, Zhou Y, Huang H, Shan S, Fulham MJ, Feng DD: Lung nodule classification with multilevel patch-based context analysis. IEEE Transactions on Biomedical Engineering 2014.

  • Zhao X, Liu L, Qi S, Teng Y, Li J, Qian W. Agile convolutional neural network for pulmonary nodule classification using CT images. Int J Comput Assist Radiol Surg. 2018;13:585–95. https://doi.org/10.1007/s11548-017-1696-0.

    Article  Google Scholar 

  • Zhao J, Ji G, Qiang Y, Han X, Pei B. Shi, Z. PLoS ONE: A new method of detecting pulmonary nodules with PET/CT based on an improved watershed algorithm; 2015.

    Google Scholar 

  • Zhao J, Ji G, Han X, Qiang Y, Liao X. An automated pulmonary parenchyma segmentation method based on an improved region growing algorithm PET-CT imaging. Sci: Front. Comput; 2016.

    Google Scholar 

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Adiraju, R.V., Elias, S. A survey on lung CT datasets and research trends. Res. Biomed. Eng. 37, 403–418 (2021). https://doi.org/10.1007/s42600-021-00138-3

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