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Incremental Learning with Selective Memory (ILSM): Towards Fast Prostate Localization for Image Guided Radiotherapy

  • Yaozong Gao
  • Yiqiang Zhan
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8150)

Abstract

Image-guided radiotherapy (IGRT) requires fast and accurate localization of prostate in treatment CTs, which is challenging due to low tissue contrast and large anatomical variations across patients. On the other hand, in IGRT workflow, a series of CT images is acquired from the same patient under treatment, which contains valuable patient-specific information yet is often neglected by previous works. In this paper, we propose a novel learning framework, namely incremental learning with selective memory (ILSM), to effectively learn the patient-specific appearance characteristics from these patient-specific images. Specifically, starting with a population-based discriminative appearance model, ILSM aims to “personalize” the model to fit patient-specific appearance characteristics. Particularly, the model is personalized with two steps, backward pruning that discards obsolete population-based knowledge, and forward learning that incorporates patient-specific characteristics. By effectively combining the patient-specific characteristics with the general population statistics, the incrementally learned appearance model can localize the prostate of the specific patient much more accurately. Validated on a large dataset (349 CT scans), our method achieved high localization accuracy (DSC ~0.87) in 4 seconds.

Keywords

Training Image Appearance Model Incremental Learning Prostate Motion High Localization Accuracy 
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.

References

  1. 1.
    Liu, W., Qian, J., Hancock, J.L., Xing, L., Luxton, G.: Clinical development of a failure detection-based online repositioning strategy for prostate IMRT—Experiments, simulation, and dosimetry study. Med. Phys. 37(10), 5287–5297 (2010)CrossRefGoogle Scholar
  2. 2.
    Costa, M.J., Delingette, H., Novellas, S., Ayache, N.: Automatic segmentation of bladder and prostate using coupled 3D deformable models. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 252–260. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    Chen, S., Lovelock, D.M., Radke, R.J.: Segmenting the prostate and rectum in CT imagery using anatomical constraints. Medical Image Analysis 15(1), 1–11 (2011)CrossRefzbMATHGoogle Scholar
  4. 4.
    Foskey, M., Brad, D., Lav, G., Ed, C., Nathalie, S., Sandrine, T., Julian, R., Sarang, J.: Large deformation 3D image registration in image-guided radiation therapy. Physics in Medicine and Biology 50(24), 5869–5892 (2005)CrossRefGoogle Scholar
  5. 5.
    Liao, S., Shen, D.: A Feature-Based Learning Framework for Accurate Prostate Localization in CT Images. IEEE TIP 21(8), 3546–3559 (2012)MathSciNetGoogle Scholar
  6. 6.
    Viola, P., Jones, M.: Robust Real-Time Face Detection. IJCV 57(2), 137–154 (2004)CrossRefGoogle Scholar
  7. 7.
    Zhan, Y., Dewan, M., Harder, M., Krishnan, A., Zhou, X.S.: Robust Automatic Knee MR Slice Positioning Through Redundant and Hierarchical Anatomy Detection. IEEE TMI 30(12), 2087–2100 (2011)Google Scholar
  8. 8.
    Zhan, Y., Zhou, X.S., Peng, Z., Krishnan, A.: Active Scheduling of Organ Detection and Segmentation in Whole-Body Medical Images. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 313–321. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM. 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Zhang, S., Zhan, Y., Dewan, M., Huang, J., Metaxas, D.N., Zhou, X.S.: Deformable Segmentation via Sparse Shape Representation. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 451–458. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yaozong Gao
    • 1
    • 2
  • Yiqiang Zhan
    • 3
  • Dinggang Shen
    • 1
  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillUSA
  2. 2.Department of Computer ScienceUniversity of North Carolina at Chapel HillUSA
  3. 3.Siemens Medical Solutions USA, Inc.MalvernUSA

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