Active Scheduling of Organ Detection and Segmentation in Whole-Body Medical Images

  • Yiqiang Zhan
  • Xiang Sean Zhou
  • Zhigang Peng
  • Arun Krishnan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5241)

Abstract

With the advance of whole-body medical imaging technologies, computer aided detection/diagnosis (CAD) is being scaled up to deal with multiple organs or anatomical structures simultaneously. Multiple tasks (organ detection/segmentation) in a CAD system are often highly dependent due to the anatomical context within a human body. In this paper, we propose a method to schedule multi-organ detection/segmentation based on information theory. The central idea is to schedule tasks in an order that each operation achieves maximum expected information gain. The scheduling rule is formulated to embed two intuitive principles: (1) a task with higher confidence tends to be scheduled earlier; (2) a task with higher predictive power for other tasks tends to be scheduled earlier. More specifically, task dependency is modeled by conditional probability; the outcome of each task is assumed to be probabilistic as well; and the scheduling criterion is based on the reduction of the summed conditional entropy over all tasks. The validation is carried out on two challenging CAD problems, multi-organ detection in whole-body CT and liver segmentation in PET-CT. Compared to unscheduled and ad hoc scheduled organ detection/segmentation, our scheduled execution achieves higher accuracy with faster speed.

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References

  1. 1.
    Visvikis, D., Ell, P.J.: Impact of technology on the utilisation of positron emission tomography in lymphoma: current and future perspectives. European Journal of Nuclear Medicine and Molecular Imaging 30, S106–S116 (2002)CrossRefGoogle Scholar
  2. 2.
    Niitsu, M., Takeda, T.: Solitary hot spots in the ribs on bone scan: value of thin-section reformatted computed tomography to exclude radiography-negative fractures. J. Comput. Assit. Tomogr. 27, 469–474 (2003)CrossRefGoogle Scholar
  3. 3.
    O’Connell, M., Powell, T., Brennan, D., Lynch, T., McCarthy, C., Eustace, S.: Whole-body MR imaging in the diagnosis of polymyositis. AJR Am. J. Roentgenol. 179, 967–971 (2002)Google Scholar
  4. 4.
    Weber, U., Pfirrmann, C.W., Kissling, R.O., Hodler, J., Zanetti, M.: Whole body mr imaging in ankylosing spondylitis: a descriptive pilot study in patients with suspected early and active confirmed ankylosing spondylitis. BMC Musculoskeletal Disorders 8 (2007)Google Scholar
  5. 5.
    Brucker, P.: Scheduling algorithms (2004)Google Scholar
  6. 6.
    Pruhs, K., Sgall, J., Torng, E.: Handbook of Scheduling: Algorithms, Models, and Performance Analysis. CRC Press, Boca Raton (2003)Google Scholar
  7. 7.
    Nam, I.: Dynamic scheduling for a flexible processing network. Operations Research 49, 305–315 (2001)MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Chou, M.C., Liu, H., Queyranne, M., Simchi-Levi, D.: On the asymptotic optimality of a simple on-line algorithm for the stochastic single-machine weighted completion time problem and its extensionsbrownian models of open processing networks:canonical representation of workload. Operations Research 54, 464–474 (2006)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Denzler, J., Brown, C.M.: Information theoretic sensor data selection for active object recognition and state estimation. IEEE Trans. PAMI 24, 145–157 (2002)Google Scholar
  10. 10.
    Cover, T., Thomas, J.: Elements of information theory (1991)Google Scholar
  11. 11.
    Viola, P., Jones, M.J.: Robust real-time face detection. International Journal of Computer Vision 57, 137–154 (2004)CrossRefGoogle Scholar
  12. 12.
    Florin, C., Paragios, N., Funka-Lea, G., Williams, J.: Liver segmentation using sparse 3D prior models with optimal data support. IPMI (2007)Google Scholar
  13. 13.
    Zhan, Y., Shen, D.: Deformable segmentation of 3-D ultrasound prostate images using statistical texture matching method. IEEE Trans. Med. Imaging 25, 256–272 (2005)Google Scholar
  14. 14.
    Bookstein, F.: Principal warps: thin-plate splines and the decompotion of deformations. IEEE Trans. PAMI 11, 567–585 (1989)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yiqiang Zhan
    • 1
  • Xiang Sean Zhou
    • 1
  • Zhigang Peng
    • 1
  • Arun Krishnan
    • 1
  1. 1.Siemens Medical Solutions USA, Inc.Malvern 

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