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Deformable Reconstruction of Histology Sections Using Structural Probability Maps

  • Markus Müller
  • Mehmet Yigitsoy
  • Hauke Heibel
  • Nassir Navab
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

The reconstruction of a 3D volume from a stack of 2D histology slices is still a challenging problem especially if no external references are available. Without a reference, standard registration approaches tend to align structures that should not be perfectly aligned. In this work we introduce a deformable, reference-free reconstruction method that uses an internal structural probability map (SPM) to regularize a free-form deformation. The SPM gives an estimate of the original 3D structure of the sample from the misaligned and possibly corrupted 2D slices. We present a consecutive as well as a simultaneous reconstruction approach that incorporates this estimate in a deformable registration framework. Experiments on synthetic and mouse brain datasets indicate that our method produces similar results compared to reference-based techniques on synthetic datasets. Moreover, it improves the smoothness of the reconstruction compared to standard registration techniques on real data.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Markus Müller
    • 1
  • Mehmet Yigitsoy
    • 1
  • Hauke Heibel
    • 3
  • Nassir Navab
    • 1
    • 2
  1. 1.Computer Aided Medical ProceduresTechnische Universität MünchenGermany
  2. 2.Computer Aided Medical ProceduresJohns Hopkins UniversityUSA
  3. 3.microDimensions GmbHMünchenGermany

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