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Überatlas : Fast and robust registration for multi-atlas segmentation

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Author

  • Jennifer Alvén
  • Alexander Norlén
  • Olof Enqvist
  • Fredrik Kahl

Summary, in English

Multi-atlas segmentation has become a frequently used tool for medical image segmentation due to its outstanding performance. A computational bottleneck is that all atlas images need to be registered to a new target image. In this paper, we propose an intermediate representation of the whole atlas set – an überatlas – that can be used to speed up the registration process. The representation consists of feature points that are similar and detected consistently throughout the atlas set. A novel feature-based registration method is presented which uses the überatlas to simultaneously and robustly find correspondences and affine transformations to all atlas images. The method is evaluated on 20 CT images of the heart and 30 MR images of the brain with corresponding ground truth. Our approach succeeds in producing better and more robust segmentation results compared to three baseline methods, two intensity-based and one feature-based, and significantly reduces the running times.

Publishing year

2016-09-01

Language

English

Pages

249-255

Publication/Series

Pattern Recognition Letters

Volume

80

Document type

Journal article

Publisher

Elsevier

Topic

  • Medical Image Processing

Keywords

  • Brain segmentation
  • Feature-based registration
  • Multi-atlas segmentation
  • Pericardium segmentation

Status

Published

ISBN/ISSN/Other

  • ISSN: 0167-8655