Agreement of CAD features with expert observer ratings for characterization of pulmonary nodules in CT using the LIDC-IDRI database

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Volumn 7260, Issue , 2009, Pages

  Wiemker, Rafael ^a   Bergtholdt, Martin ^a   Dharaiya, Ekta ^b   Kabus, Sven ^a   Lee, Michael C ^c  

^a PHILIPS RESEARCH LABORATORIES   (Germany)

^b Philips Medical System CT   (United States)

^c PHILIPS RESEARCH LABORATORIES   (Netherlands)

Author keywords

Computer aided diagnosis; IDRI database; Indeterminate nodule; LIDC database; Nodule characteristics; Nodule shape; Shape index; Solitary pulmonary nodule

Indexed keywords

IDRI DATABASE; INDETERMINATE NODULE; LIDC DATABASE; NODULE CHARACTERISTICS; NODULE SHAPE; SHAPE INDEX; SOLITARY PULMONARY NODULE;

MEDICAL IMAGING; OBSERVABILITY; POSITRON EMISSION TOMOGRAPHY; REGRESSION ANALYSIS;

COMPUTER AIDED DIAGNOSIS;

EID: 66749122558     PISSN: 16057422     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.811569     Document Type: Conference Paper

References (36)

1. Erasmus, J.J., Connolly, J.E., McAdams, H.P., Roggli, V.L., Solitary Pulmonary Nodules: Part I. Morphologic Evaluation for Differentiation of Benign and Malignant Lesions, Radiographics 20, p.43-58, (2000).
2. Erasmus, J.J., McAdams, H.P., Connolly, J.E., Solitary Pulmonary Nodules: Part II. Evaluation of the Indeterminate Nodule, Radiographics 20, p.59-66, 2000.
3. Winer-Muram, H.T., The Solitary Pulmonary Nodule, Radiology 239(1), p.34-49, 2006.
4. Jeong, Y.J., Yi, C.A., Lee, K.S., Solitary Pulmonary Nodules: Detection, Characterization, and Guidance for Further Diagnostic Workup and Treatment, American Journal of Roentgenology 188, p.57-68, 2007. (Pubitemid 46012490)
5. M. Das, J. Ley-Zaporozhan, H. Gietema, A. Czech, G. Mühlenbruch, A. Mahnken, M. Katoh, A. Bakai, M. Salganicoff, S. Diederich, M. Prokop, H.-U. Kauczor, R. Günther, J. Wildberger, Accuracy of automated volumetry of pulmonary nodules across different multislice CT scanners. Eur. Radiology 2007. (Pubitemid 47097455)
6. Wiemker, R., Rogalla, P., Blaffert, T., Sifri, D., Hay, O., Shah, E., Truyen, R., Fleiter, T., Aspects of Computer Aided Detection (CAD) and Volumetry of Pulmonary Nodules Using Multislice CT, British Journal of Radiology 78, Special Issue, p.S46-S56, 2005. (Pubitemid 40932507)
7. B.J. de Hoop, H. Gietema, B. van Ginneken, P. Zanen, G. Groenewegen, M. Prokop, A comparison of 6 software packages for evaluation of solid lung nodules using semi-automated volumetry: What is the minimum increase in size to detect growth in repeated CT examinations, European Radiology, 2008.
8. M. F. McNitt-Gray, E.M. Hart, N.Wyckoff, J.W. Sayre, J. G. Goldin, and D. R. Aberle, A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution CT: Preliminary results, Medical Physics, vol.26, no. 6, pp. 880-888, 1999. (Pubitemid 29284105)
9. Y. Matsuki, K. Nakamura, H. Watanabe, T. Aoki, H. Nakata, S. Katsuragawa, K. Doi, Usefulness of an artificial neural network for differentiating benign from malignant pulmonary nodules on high-resolution CT: Evaluation with receiver operating characteristic analysis, American Journal of Roentgenology 178 (3), p.657-663, 2002. (Pubitemid 34178147)
10. Kawata, Y., Niki, N., Ohmatsu, H., Kusumoto, M. Kakinuma, R., Mori, K., Nishiyama, H., Eguchi, K. Kaneko, M. Moriyama, N., Computer aided classification of pulmonary nodules in surrounding and internal feature spaces using three-dimensional thoracic CT images, Proc. SPIE Medical Imaging 2002, SPIE vol. 4684, 2002. (Pubitemid 35237307)
11. S.-C. B. Lo, M. T. F. Li-Yueh Hsu and, Y.-M. F. Lure, and H. Zhao, Classification of lung nodules in diagnostic CT: An approach based on 3-D vascular features, nodule density distributions, and shape features, Proc. SPIE Medical Imaging, SPIE vol. 5032, p.183-189, 2003.
12. Armato SG III, Altman MB, Wilkie J, Sone S, Li F, Doi K, Roy AS: Automated lung nodule classification following automated nodule detection on CT: A serial approach, Medical Physics 30, p.1188-1197, 2003. (Pubitemid 36735985)
13. M. Aoyama, Q. Li, S. Katsuragawa, F. Li, S. Sone, and K. Doi, Computerized scheme for determination of the likelihood measure of malignancy for pulmonary nodules on low-dose CT images, Medical Physics 30 (3), p.387- 394, 2003. (Pubitemid 36337105)
14. Wang, Y., McNitt-Gray, M.F., Brown, M.S., Goldin, J.G., Zhao, H., Aberle, D.R., Differentiating solitary pulmonary nodules (SPNs) with 3D shape features, Proc. SPIE Medical Imaging 2007, SPIE vol. 6514, 2007. (Pubitemid 47567637)
15. Raicu DS, Varutbangkul E, Cisneros JG, Furst JD, Channin DS, Armato SG III: Semantics and image content integration for pulmonary nodule interpretation in thoracic computed tomography. Proc. SPIE Medical Imaging, SPIE vol. 6514, 2007. (Pubitemid 350126935)
16. Lung Image Database Consortium: http://imaging.cancer.gov/ programsandresources/InformationSystems/LIDC
17. McNitt-Gray MF, Armato SG 3rd, Meyer CR, Reeves AP, McLennan G, Pais RC, Freymann J, Brown MS, Engelmann RM, Bland PH, Laderach GE, Piker C, Guo J, Towfic Z, Qing DP, Yankelevitz DF, Aberle DR, van Beek EJ, MacMahon H, Kazerooni EA, Croft BY, Clarke LP. The Lung Image Database Consortium (LIDC) data collection process for nodule detection and annotation, Acad Radiol. 14 (12): 1464-1474, 2007. (Pubitemid 350116641)
18. Meyer CR, Johnson TD, McLennan G, Aberle DR, Kazerooni EA, MacMahon H, Mullan BF, Yankelevitz DF, van Beek EJR, Armato SG III, McNitt-Gray MF, Reeves AP, Gur D, Henschke CI, Hoffman EA, Bland PH, Laderach G, Pais R, Qing D, Piker C, Guo J, Starkey A, Max D, Croft BY, Clarke LP: Evaluation of lung MDCT nodule annotation across radiologists and methods. Academic Radiology 13: 1254-1265, 2006. (Pubitemid 44345260)
19. Reeves AP, Biancardi AM, Apanasovich TV, Meyer CR, MacMahon H, van Beek EJR, Kazerooni EA, Yankelevitz DF, McNitt-Gray MF, McLennan G, Armato SG III, Henschke CI, Aberle DR, Croft BY, Clarke LP: The Lung Image Database Consortium (LIDC): A comparison of different size metrics for pulmonary nodule measurements. Academic Radiology 14, p.1475-1485, 2007. (Pubitemid 350116645)
20. Armato SG III, McNitt-Gray MF, Reeves AP, Meyer CR, McLennan G, Aberle DR, Kazerooni EA, MacMahon H, van Beek EJ, Yankelevitz D, Hoffman EA, Henschke CI, Roberts RY, Brown MS, Engelmann RM, Pais RC, Piker CW, Qing D, Kocherginsky M, Croft BY, Clarke LP, The Lung Image Database Consortium (LIDC): An evaluation of radiologist variability in the identification of lung nodules on CT scans, Acad Radiol. 14 (11), 2007. (Pubitemid 47615519)
21. S.G. Armato III, L. Bidaut, B. Zhao, M. Salganicoff, U. Shreter, L.P. Clarke, B.Y. Croft, G. McLennan, M.F. McNitt-Gray, C.R. Meyer, A.P. Reeves, D.R. Aberle, E.A. Kazerooni, H. MacMahon, E.J.R. van Beek, D. Yankelevitz, Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Reference Database for Thoracic CT Image Analysis Research, RSNA 2008.
22. R. Opfer, R. Wiemker, A new General Tumor Segmentation Framework Based on Radial Basis Function Energy Minimization with a Validation Study on LIDC Lung Nodules, Proc. SPIE Medical Imaging vol. 6512, 2007. (Pubitemid 350126950)
23. J. Koenderink. Solid shape. The MIT Press, 1990.
24. Kawata Y, Niki N, Ohmatsu H, Kusumoto M, Kakinuma R, Mori K, Nishiyama H, Eguchi K, Kaneko M, Moriyama N, Curvature-based characterization of shape and internal intensity structure for classification of pulmonary nodules using thin-section CT images, Proc. SPIE Medical Imaging Vol. 3661, p. 541-552, 1999.
25. Yang Wang, Michael F. Mcnitt-Gray, Matthew S. Brown, Jonathan G. Goldin, Haiming Zhao, and Denise R. Aberle, Differentiating solitary pulmonary nodules (SPNs) with 3D shape features, Proc. SPIE Medical Imaging, vol.6514, 2007. (Pubitemid 47567637)
26. K. Murphy, A. Schilham, H. Gietema, M. Prokop, B. van Ginneken, Automated detection of pulmonary nodules from low-dose computed tomography scans using a two-stage classification system based on local image features, Proc. SPIE Medical Imaging, vol.6514, 2007. (Pubitemid 47567558)
27. Yoshida, H., Masutani, Y., MacEneaney, P., Rubin, D.T., Dachman, A.H., Computerized Detection of Colonic Polyps at CT Colonography on the Basis of Volumetric Features: Pilot Study, Radiology 222: 327-336, 2002. (Pubitemid 34106100)
28. R. Wiemker, R. Opfer, T. Bülow, S. Kabus, E. Dharaiya, Repeatability and noise robustness of spicularity features for computer-aided characterization of pulmonary nodules in CT, Proc. SPIE Medical Imaging 2008, SPIE vol. 6915-36.
29. J. Cohen: A coefficient of agreement for nominal scales, Educat. and Psychol. Measurement. 20, p.37-46, 1960.
30. J. Cohen: Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit, Psychological Bulletin, p.213-220, 1968.
31. H.L. Kundel, M. Polansky, Measurement of Observer Agreement, Radiology 228 (2), 2003. (Pubitemid 36910064)
32. J. L. Fleiss, Statistical methods for rates and proportions, Wiley, 1981.
33. Weka Data Mining Software, University of Waikato, http://www.cs.waikato. ac.nz/~ml/weka/
34. Chih-Chung Chang and Chih-Jen Lin, LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
35. V. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, 1995.
36. Alex J. Smola and Bernhard Schölkopf, A tutorial on support vector regression, Statistics and Computing 14(3), p.199-222, 2004. (Pubitemid 39063488)