Advertisement

Automatic Compartment Modelling and Segmentation for Dynamical Renal Scintigraphies

  • Daniel Ståhl
  • Kalle Åström
  • Niels Christian Overgaard
  • Matilda Landgren
  • Karl Sjöstrand
  • Lars Edenbrandt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6688)

Abstract

Time-resolved medical data has important applications in a large variety of medical applications. In this paper we study automatic analysis of dynamical renal scintigraphies. The traditional analysis pipeline for dynamical renal scintigraphies is to use manual or semiautomatic methods for segmentation of pixels into physical compartments, extract their corresponding time-activity curves and then compute the parameters that are relevant for medical assessment. In this paper we present a fully automatic system that incorporates spatial smoothing constraints, compartment modelling and positivity constraints to produce an interpretation of the full time-resolved data. The method has been tested on renal dynamical scintigraphies with promising results. It is shown that the method indeed produces more compact representations, while keeping the residual of fit low. The parameters of the time activity curve, such as peak-time and time for half activity from peak, are compared between the previous semiautomatic method and the method presented in this paper. It is also shown how to obtain new and clinically relevant features using our novel system.

Keywords

Medical image analysis time-resolved compartment modelling dynamical renal scintigraphies segmentation 

References

  1. 1.
    Veltri, P., Vecchio, A., Carbone, V.: Proper orthogonal decomposition analysis of spatio-temporal behavior of renal scintigraphies. Physica Medica 26, 57–70 (2010)CrossRefGoogle Scholar
  2. 2.
    Piepsz, A., Sixt, R., Gordon, I.: Performing renography in children with antenatally detected pelvi-ureteric junction stenosis: errors, pitfalls, controversies. The Quarterly Journal of Nuclear Medicine and Molecular Imaging (August 2010)Google Scholar
  3. 3.
    Lawson, R.: Renogram Processing - The Manchester Method. In: XIV. International Symposium on Radionuclides in Nephrourology, Mikulov, Czech Republic, May 11-14 (2010)Google Scholar
  4. 4.
  5. 5.
    Prigent, A., Cosgriff, P., Gates, G.F., Granerus, G., Fine, E.J., Itoh, K., Peters, M., Piepsz, A., Rehling, M., Rutland, M., Taylor Jr, A.: Consensus Report on Quality Control of Quantitative Measurements of Renal Function Obtained From the Renogram: International Consensus Committee From the Scientific committee of Radionuclides in Nephrourology. Seminars in Nucl. Med. 29, 146–159 (1999)CrossRefGoogle Scholar
  6. 6.
    Lawson, R.: Quantitative Methods in Renography. In: XIV. International Symposium on Radionuclides in Nephrourology, Mikulov, Czech Republic, May 11-14 (2010)Google Scholar
  7. 7.
    Rutland, M.D.: A comprehensive analysis of renal DTPA studies. Theory and normal values. Nuc. Med. Comm. 6, 11–20 (1985)CrossRefGoogle Scholar
  8. 8.
    Durand, E., Blaufox, M.D., Britton, K.E., Carlsen, O., Cosgriff, P., Fine, E., Fleming, J., Nimmon, C., Piepsz, A., Prigent, A., Samal, M.: International Scientific Committee of Radionuclides in Nephrourology (ISCORN) Consensus on Renal Transit Time Measurement. Semin. Nucl. Med. 38, 82–102 (2008)CrossRefGoogle Scholar
  9. 9.
    Moonen, M.: Gamma Camera Renography with 99mTc-DTPA; Assessment of Total and Split Renal Function, Gothenburg (1994)Google Scholar
  10. 10.
    Fine, D.R., Lurie, R.E., Candy, G.P.: An anatomical and physiological model of the renal parenchyma - model development and parametric identification. Physiol. Meas. 15, 407–428 (1994)CrossRefGoogle Scholar
  11. 11.
    Meng, L.K., Ng, D., Ghista, D.N., Rudolph, H.: Quantitation of Renal Function based on Two-Compartmental Modeling Renal Pelvis. In: Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China, September 1-4 (2005)Google Scholar
  12. 12.
    Coffey, J.P.: Analysis of compartmental models of type 4 nuclear renograms with calculation of flow rate parameters and instantaneous drainage rates. In: BJU International, vol. 92, pp. 85–91 (2003)Google Scholar
  13. 13.
    Golub, G.H., van Loan, C.F.: Matrix Computations. The Johns Hopkins University Press (1996)Google Scholar
  14. 14.
    Bookstein, F.L.: Principal Warps: Thin Plate Splines and the Decomposition of Deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence 11(6) (June 1989)Google Scholar
  15. 15.
    Hastie, T., Tibshirani, R., Friedman, J.: Elements of Statistical Learning. Springer, New York (2008)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Daniel Ståhl
    • 1
    • 2
  • Kalle Åström
    • 1
  • Niels Christian Overgaard
    • 1
  • Matilda Landgren
    • 1
    • 2
  • Karl Sjöstrand
    • 2
    • 3
  • Lars Edenbrandt
    • 2
  1. 1.Centre for Mathematical SciencesLund UniversityLundSweden
  2. 2.Exini Diagnostics ABLundSweden
  3. 3.Department of Informatics and Mathematical ModellingTechnical University of DenmarkKgs. LyngbyDenmark

Personalised recommendations