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A Statistical Shape Model for the Liver

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 2489)

Abstract

The use of statistical shape models is a promising approach for robust segmentation of medical images. One of the major challenges in building a 3D shape model from a training set of segmented instances of an object is the determination of the correspondence between them. We propose a novel geometric approach that is based on minimizing the distortion of the mapping between two surfaces. In this work we investigate the accuracy and completeness of a 3D statistical shape model for the liver built from 20 manually segmented individual CT data sets. The quality of the shape model is crucial for its application as a segmentation tool.

Keywords

  • Shape Model
  • Statistical Shape Model
  • Robust Segmentation
  • Segmented Instance
  • Surface Correspondence

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.

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© 2002 Springer-Verlag Berlin Heidelberg

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Lamecker, H., Lange, T., Seebass, M. (2002). A Statistical Shape Model for the Liver. In: Dohi, T., Kikinis, R. (eds) Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002. MICCAI 2002. Lecture Notes in Computer Science, vol 2489. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45787-9_53

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  • DOI: https://doi.org/10.1007/3-540-45787-9_53

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44225-7

  • Online ISBN: 978-3-540-45787-9

  • eBook Packages: Springer Book Archive

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