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Hierarchical Approach for Neonate Cerebellum Segmentation from MRI: An Experimental Study

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Mathematical Morphology and Its Applications to Signal and Image Processing (ISMM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11564))

Abstract

Morphometric analysis of brain structures is of high interest for premature neonates, in particular for defining predictive neurodevelopment biomarkers. This requires beforehand, the correct segmentation of structures of interest from MR images. Such segmentation is however complex, due to the resolution and properties of data. In this context, we investigate the potential of hierarchical image models, and more precisely the binary partition tree, as a way of developing efficient, interactive and user-friendly 3D segmentation methods. In particular, we experiment the relevance of texture features for defining the hierarchy of partitions constituting the final segmentation space. This is one of the first uses of binary partition trees for 3D segmentation of medical images. Experiments are carried out on 19 MR images for cerebellum segmentation purpose.

The research leading to these results has been supported by the ANR MAIA project (http://recherche.imt-atlantique.fr/maia), grant ANR-15-CE23-0009 of the French National Research Agency; and the American Memorial Hospital Foundation.

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Notes

  1. 1.

    © Imperial College of Science, Technology and Medicine and I. S. Gousias 2013.

  2. 2.

    Epirmex is a part of the French epidemiologic study Epipage 2 [1], http://epipage2.inserm.fr.

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Author information

Authors and Affiliations

  1. Université de Reims Champagne-Ardenne, CReSTIC, Reims, France

    Pierre Cettour-Janet, Gilles Valette, Laurent Lucas, Gauthier Loron, Nathalie Bednarek & Nicolas Passat

  2. Service de médecine néonatale et réanimation pédiatrique, CHU de Reims, Reims, France

    Hélène Meunier, Gauthier Loron & Nathalie Bednarek

  3. IMT Atlantique, LaTIM U1101 INSERM, UBL, Brest, France

    François Rousseau

Authors
  1. Pierre Cettour-Janet
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  2. Gilles Valette
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  3. Laurent Lucas
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  4. Hélène Meunier
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  7. François Rousseau
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  8. Nicolas Passat
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Correspondence to Pierre Cettour-Janet .

Editor information

Editors and Affiliations

  1. Universität des Saarlandes, Saarbrücken, Germany

    Bernhard Burgeth

  2. Forschungszentrum Jülich GmbH, Jülich, Germany

    Andreas Kleefeld

  3. Université de Strasbourg, Illkirch Cedex, France

    Benoît Naegel

  4. Université de Reims Champagne-Ardenne, Reims Cedex 2, France

    Nicolas Passat

  5. Université Paris-Est, Noisy le Grand Cedex, France

    Benjamin Perret

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Cettour-Janet, P. et al. (2019). Hierarchical Approach for Neonate Cerebellum Segmentation from MRI: An Experimental Study. In: Burgeth, B., Kleefeld, A., Naegel, B., Passat, N., Perret, B. (eds) Mathematical Morphology and Its Applications to Signal and Image Processing. ISMM 2019. Lecture Notes in Computer Science(), vol 11564. Springer, Cham. https://doi.org/10.1007/978-3-030-20867-7_37

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  • DOI: https://doi.org/10.1007/978-3-030-20867-7_37

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