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Nonlocal Patch-Based Label Fusion for Hippocampus Segmentation

  • Pierrick Coupé
  • José V. Manjón
  • Vladimir Fonov
  • Jens Pruessner
  • Montserrat Robles
  • D. Louis Collins
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6363)

Abstract

Quantitative magnetic resonance analysis often requires accurate, robust and reliable automatic extraction of anatomical structures. Recently, template-warping methods incorporating a label fusion strategy have demonstrated high accuracy in segmenting cerebral structures. In this study, we propose a novel patch-based method using expert segmentation priors to achieve this task. Inspired by recent work in image denoising, the proposed nonlocal patch-based label fusion produces accurate and robust segmentation. During our experiments, the hippocampi of 80 healthy subjects were segmented. The influence on segmentation accuracy of different parameters such as patch size or number of training subjects was also studied. Moreover, a comparison with an appearance-based method and a template-based method was carried out. The highest median kappa value obtained with the proposed method was 0.884, which is competitive compared with recently published methods.

Keywords

Patch Size Segmentation Accuracy Training Subject Nonlinear Registration Stereotaxic Space 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Pierrick Coupé
    • 1
  • José V. Manjón
    • 2
  • Vladimir Fonov
    • 1
  • Jens Pruessner
    • 1
  • Montserrat Robles
    • 2
  • D. Louis Collins
    • 1
  1. 1.McConnell Brain Imaging Centre, Montreal Neurological InstituteMcGill University, Montreal, Canada UniversityMontrealCanada
  2. 2.Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA)Universidad Politécnica de ValenciaValenciaSpain

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