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Learning-Boosted Label Fusion for Multi-atlas Auto-Segmentation

  • Xiao Han
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8184)

Abstract

Structure segmentation of patient CT images is an essential step for radiotherapy planning but very tedious if done manually. Atlas-based auto-segmentation (ABAS) methods have shown great promise for getting accurate segmentation results especially when multiple atlases are used. In this work, we aim to further improve the performance of ABAS by integrating it with learning-based segmentation techniques. In particular, the Random Forests (RF) supervised learning algorithm is applied to construct voxel-wise structure classifiers using both local and contextual image features. Training of the RF classifiers is specially tailored towards structure border regions where errors in ABAS segmentation typically occur. The trained classifiers are applied to re-estimate structure labels at “ambiguous” voxels where labels from different atlases do not fully agree. The classification result is combined with traditional label fusion to achieve improved accuracy. Experimental results on H&N images and ribcage segmentation show clear advantage of the proposed method, which offers consistent and significant improvements over the baseline method.

Keywords

atlas-based segmentation machine learning label fusion random forests radiotherapy planning CT image 

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References

  1. 1.
    Pekar, V., Allaire, S., Kim, J., Jaffray, D.A.: Head and Neck Auto-segmentation Challenge. In: van Ginneken, B., Murphy, K., Heimann, T., Pekar, V., Deng, X. (eds.) Medical Image Analysis for the Clinic: A Grand Challenge, pp. 273–280. Springer, Heidelberg (2010)Google Scholar
  2. 2.
    Rohlfing, T., Brandt, R., Menzel, R., Russakoff, D.B., Maurer Jr., C.R.: Quo Vadis, Atlas-based segmentation? In: Suri, J., Wilson, D., Laxminarayan, S. (eds.) The Handbook of Medical Image Analysis. Kluwer (2005)Google Scholar
  3. 3.
    Criminisi, A., Shotton, J.: Decision forests for computer vision and medical image analysis. Springer, London (2013)CrossRefGoogle Scholar
  4. 4.
    Powell, S., Magnotta, V.A., Johnson, H., Jammalamadaka, V.K., Andreasen, N.C., Pierson, R.: Registration and machine learning based automated segmentation of subcortical and cerebellar brain structures. NeuroImage 39, 238–247 (2008)CrossRefGoogle Scholar
  5. 5.
    Hao, Y., Liu, J., Duan, Y., Zhang, X., Yu, C., Jiang, T., Fan, Y.: Local label learning (L3) for multi-atlas based segmentation. In: Proc. SPIE, vol. 8314, p. 83142E (2012)Google Scholar
  6. 6.
    Nie, J., Shen, D.: Automated segmentation of mouse brain images using multi-atlas multi-ROI deformation and label fusion. Neuroinformatics 11, 35–45 (2013)CrossRefGoogle Scholar
  7. 7.
    Srhoj-Egekher, V., Benders, M.J.N.L., Kersbergen, K.J., Viergever, M.A., Isgum, I.: Automatic segmentation of neonatal brain MRI using atlas based segmentation and machine learning approach. In: MICCAI Grand Challenge: Neonatal Brain Segmentation (2012)Google Scholar
  8. 8.
    Han, X., Hoogeman, M., Levendag, P., Hibbard, L., Teguh, D., Voet, P., Cowen, A., Wolf, T.: Atlas-based auto-segmentation of head and neck CT images. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 434–441. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Four-chamber heart modeling and automatic segmentation for 3D cardiac CT volumes using marginal space learning and steerable features. IEEE Trans. Med. Imag. 27, 1668–1681 (2008)CrossRefGoogle Scholar
  10. 10.
    Warfield, S.K., Zou, K.H., Wells, W.M.: Simultaneous truth and performance level estimation (STAPLE): An algorithm for the validation of image segmentation. IEEE Trans. Med. Imag. 23, 903–921 (2004)CrossRefGoogle Scholar
  11. 11.
    Tu, Z., Narr, K.L., Dollar, P., Dinov, I., Thompson, P.M., Toga, A.W.: Brain anatomical structure segmentation by hybrid discriminative/generative models. IEEE Trans. Med. Imag. 27, 495–508 (2008)CrossRefGoogle Scholar
  12. 12.
    Wu, D., Liu, D., Puskas, Z., Lu, C., Wimmer, A., Teitjen, C., Soza, G., Zhou, S.K.: A learning based deformable template matching method for automatic rib centerline extraction and labeling in CT images. In: Proc. CVPR 2012 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Xiao Han
    • 1
  1. 1.Elekta Inc.St. LouisUSA

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