Fully automated and adaptive detection of amyloid plaques in stained brain sections of Alzheimer transgenic mice

  • Authors:

  • Abdelmonem Feki;Olivier Teboul;Albertine Dubois;Bruno Bozon;Alexis Faure;Philippe Hantraye;Marc Dhenain;Benoit Delatour;Thierry Delzescaux

  • Affiliations:

  • MIRCen, URA, CEA, CNRS, Orsay, France and Ecole Centrale Paris, Chatenay-Malabry, France;MIRCen, URA, CEA, CNRS, Orsay, France and Ecole Centrale Paris, Chatenay-Malabry, France;MIRCen, URA, CEA, CNRS, Orsay, France;Laboratoire NAMC, CNRS, UMR, Université Paris Sud, Orsay, France;Laboratoire NAMC, CNRS, UMR, Université Paris Sud, Orsay, France;MIRCen, URA, CEA, CNRS, Orsay, France;MIRCen, URA, CEA, CNRS, Orsay, France;Laboratoire NAMC, CNRS, UMR, Université Paris Sud, Orsay, France;MIRCen, URA, CEA, CNRS, Orsay, France

  • Venue:
  • MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
  • Year:
  • 2007

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Abstract

Automated detection of amyloid plaques (AP) in post mortem brain sections of patients with Alzheimer disease (AD) or in mouse models of the disease is a major issue to improve quantitative, standardized and accurate assessment of neuropathological lesions as well as of their modulation by treatment. We propose a new segmentation method to automatically detect amyloid plaques in Congo Red stained sections based on adaptive thresholds and a dedicated amyloid plaque/tissue modelling. A set of histological sections focusing on anatomical structures was used to validate the method in comparison to expert segmentation. Original information concerning global amyloid load have been derived from 6 mouse brains which opens new perspectives for the extensive analysis of such a data in 3-D and the possibility to integrate in vivo-post mortem information for diagnosis purposes.