Local Probabilistic Atlases and a Posteriori Correction for the Segmentation of Heart Images

  • Gaetan Galisot
  • Thierry Brouard
  • Jean-Yves Ramel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10663)


Atlas-based segmentation is a well-known method for segmentation of medical images. In particular, this method could be used in an efficient way to automatically segment heart structures in MRI or CT scans. We propose, in this paper a more adaptive and interactive atlas-based segmentation method. The model presented combines several local probabilistic atlases with a topological graph. The local atlases provide more refined information about the structures’ shape while the spatial relationships between the atlases are learned and stored in a graph. Hence, local registrations need less computational time and the image segmentation can be guided by the user in an incremental way. Following this step, a pixel classification is performed with a hidden Markov random field that integrates the learned a priori information with the pixel intensities that originate from different modalities. Finally, an a posteriori correction is performed using Adaboost classifiers in order to correct voxels in the border of the seek region and improve the precision of the results. The proposed method is tested on CT scan and MRI images of the heart coming from the MM-WHS challenge.


  1. 1.
    Zhuang, X., Shen, J.: Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI. Med. Image Anal. 31, 77–87 (2016)CrossRefGoogle Scholar
  2. 2.
    Zhuang, X., Rhode, K., Razavi, R., Hawkes, D., Ourselin, S.: A registration-based propagation framework for automatic whole heart segmentation of cardiac MRI. IEEE Trans. Med. Imaging 29, 1612–1625 (2010)CrossRefGoogle Scholar
  3. 3.
    Bai, W., Shi, W., Lediq, C., Rueckert, D.: Multi-atlas segmentation with augmented features for cardiac MR images. Med. Image Anal. 19, 98–109 (2014)CrossRefGoogle Scholar
  4. 4.
    Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., Van Der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., Montillo, A., Makris, N., Rosen, B., Dale, A.M.: Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002)CrossRefGoogle Scholar
  5. 5.
    Scherrer, B., Forbes, F., Garbay, C., Dojat, M.: Distributed local MRF models for tissue and structure brain segmentation. Trans. Med. Imaging 28, 1278–1295 (2009)CrossRefGoogle Scholar
  6. 6.
    Wang, H., Das, S.R., Altinay, M., Pluta, J., Suh, J., Avants, B., Yushkevich, P.: A general-purpuse learning-based wrapper method to correct systematic errors in automatic image segmentation: consistently improved performance in hippocampus, cortex brain segmentation. NeuroImage 55(3), 968–985 (2011)CrossRefGoogle Scholar
  7. 7.
    Fornefett, M., Rohr, K., Stiehl, H.S.: Radial basis functions with compact support for elastic registration of medical images. Image Vis. Comput. 19, 87–96 (2001)CrossRefGoogle Scholar
  8. 8.
    Freund, Y., Schapire, R.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Proceedings of the 2nd European Conference on Computational Learning Theory, vol. 55(3), pp. 23–27 (1995)Google Scholar
  9. 9.
    Wang, H., Yushkevich, P.A.: Multi-atlas segmentation with joint label fusion and corrective learning - an open source implementation. Front. Neuroinformatics 7(27), 27 (2013)Google Scholar
  10. 10.
    Nyul, L.G., Udupa, J.K., Zhang, X.: New variants of a method of MRI scale standardization. Trans. Med. Imaging 19, 143–150 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Gaetan Galisot
    • 1
  • Thierry Brouard
    • 1
  • Jean-Yves Ramel
    • 1
  1. 1.LI ToursUniversité Francois-RabelaisToursFrance

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