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Mapping Multi-Modal Routine Imaging Data to a Single Reference via Multiple Templates

  • Johannes Hofmanninger
  • Bjoern Menze
  • Marc-André Weber
  • Georg Langs
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10553)

Abstract

Population level analysis of medical imaging data relies on finding spatial correspondence across individuals as a basis for local comparison of visual characteristics. Here, we describe and evaluate a framework to normalize routine images covering different parts of the human body, in different modalities to a common reference space. The framework performs two basic steps towards normalization: (1) The identification of the location and coverage of the human body by an image and (2) a non-linear mapping to the common reference space. Based on these mappings, either coordinates, or label-masks can be transferred across a population of images. We evaluate the framework on a set of routine CT and MR scans exhibiting large variability on location and coverage. A set of manually annotated landmarks is used to assess the accuracy and stability of the approach. We report distinct improvement in stability and registration accuracy compared to a classical single-atlas approach.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Johannes Hofmanninger
    • 1
  • Bjoern Menze
    • 2
  • Marc-André Weber
    • 3
    • 4
  • Georg Langs
    • 1
  1. 1.Department of Biomedical Imaging and Image-guided Therapy Computational Imaging Research LabMedical University of ViennaViennaAustria
  2. 2.Department of Computer Science & Institute for Advanced StudyTechnical University of MunichMunichGermany
  3. 3.Department of Diagnostic and Interventional RadiologyUniversity of HeidelbergHeidelbergGermany
  4. 4.Institute of Diagnostic and Interventional RadiologyRostock University Medical CenterRostockGermany

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