Supervised Probabilistic Segmentation of Pulmonary Nodules in CT Scans

  • Bram van Ginneken
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)


An automatic method for lung nodule segmentation from computed tomography (CT) data is presented that is different from previous work in several respects. Firstly, it is supervised; it learns how to obtain a reliable segmentation from examples in a training phase. Secondly, the method provides a soft, or probabilistic segmentation, thus taking into account the uncertainty inherent in this segmentation task. The method is trained and tested on a public data set of 23 nodules for which soft labelings are available. The new method is shown to outperform a previously published conventional method. By merely changing the training data, non-solid nodules can also be segmented.


Pulmonary Nodule Seed Point Multislice Compute Tomography Solid Nodule Small Pulmonary Nodule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Mullally, W., Betke, M., Wang, J.: Segmentation of nodules on chest computed tomography for growth assessment. Med. Phys. 31, 839–848 (2004)CrossRefGoogle Scholar
  2. 2.
    Kostis, W., Reeves, A., Yankelevitz, D., Henschke, C.: Three-dimensional segmentation and growth rate estimation of small pulmonary nodules in helical CT images. IEEE Trans. Med. Imag. 22(10), 1259–1274 (2003)CrossRefGoogle Scholar
  3. 3.
    Fetita, C.I., Préteux, F., Beigelman-Aubry, C., Grenier, P.: 3D automated lung nodule segmentation in HRCT. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2878, pp. 626–634. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Kuhnigk, J.M., Dicken, V., Bornemann, L., Wormanns, D., Krass, S., Peitgen, H.O.: Fast automated segmentation and reproducible volumetry of pulmonary metastases in CT-scans for therapy monitoring. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 933–941. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Okada, K., Comaniciu, D., Krishnan, A.: Robust 3D segmentation of pulmonary nodules in multislice CT images. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 881–889. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Okada, K., Comaniciu, D., Krishnan, A.: Robust anisotropic Gaussian fitting for volumetric characterization of pulmonary nodules in multislice CT. IEEE Trans. Med. Imag. 24, 409–423 (2005)CrossRefGoogle Scholar
  7. 7.
    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, 46–56 (2005)CrossRefGoogle Scholar
  8. 8.
    Sluimer, I., Schilham, A., Prokop, M., van Ginneken, B.: Computer analysis of computed tomography scans of the lung: a survey. IEEE Trans. Med. Imag. 25(4), 385–405 (2006)CrossRefGoogle Scholar
  9. 9.
    Wormanns, D., Diederich, S.: Characterization of small pulmonary nodules by CT. Eur. Radiol. 14, 1380–1391 (2004)Google Scholar
  10. 10.
    Armato, 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.: Lung Image Database Consortium: Developing a resource for the medical imaging research community. Radiology 232(3), 739–748 (2004)CrossRefGoogle Scholar
  11. 11.
    Armato, S.G., Giger, M.L., MacMahon, H.: Automated detection of lung nodules in CT scans: Preliminary results. Med. Phys. 28(8), 1552–1561 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bram van Ginneken
    • 1
  1. 1.Image Sciences InstituteUniversity Medical Center UtrechtThe Netherlands

Personalised recommendations