Atlas-based Segmentation

  • M. Bach CuadraEmail author
  • V. Duay
  • J.-Ph. Thiran


Image segmentation is a main task in many medical applications such as surgical or radiation therapy planning, automatic labelling of anatomical structures or morphological and morphometrical studies. Segmentation in medical imaging is however challenging because of problems linked to low contrast images, fuzzy object-contours, similar intensities with adjacent objects of interest, etc. Using prior knowledge can help in the segmentation task. A widely used method consists to extract this prior knowledge from a reference image often called atlas. We review in this chapter the existing approaches for atlas-based segmentation in medical imaging and we focus on those based on a volume registration method. We present the problem of using atlas information for pathological image analysis and we propose our solution for atlas-based segmentation in MR image of the brain when large space-occupying lesions are present. Finally, we present the new research directions that aim at overcome current limitations of atlas-based segmentation approaches based on registration only.


Active Contour Deformation Field Segmentation Task Tumor Growth Model Visible Contour 
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.



Our acknowledgment goes to Prof. Reto Meuli from the Radiology Department of the Lausanne Hospital (CHUV) and to Dr. Simon Warfield from Harvard Medical School for providing the patient images. Also, we thank Prof. Ron Kikinis who has provided us with the digitized atlas of the Harvard Medical School. This work has been supported by Center for Biomedical Imaging (CIBM) of the Geneva - Lausanne Universities, the EPFL, and the foundations Leenaards and Louis-Jeantet, as well as by the Swiss National Science Foundation under grant number 205320-101621.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Radiology, Center for Biomedical ImagingLausanne University Center (CHUV) and University of Lausanne (UNIL)LausanneSwitzerland
  2. 2.Department of Industrial TechnologyUniversity of Applied Sciences Western Switzerland Technology, Architecture and LandscapeGenevaSwitzerland
  3. 3.Signal processing Laboratory (LTS5)Swiss Federal Institute of Technology Lausanne (EPFL)LausanneSwitzerland

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