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Überatlas : Fast and robust registration for multi-atlas segmentation

Alvén, Jennifer; Norlén, Alexander; Enqvist, Olof and Kahl, Fredrik LU (2016) In Pattern Recognition Letters 80. p.249-255
Abstract

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... (More)

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.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Brain segmentation, Feature-based registration, Multi-atlas segmentation, Pericardium segmentation
in
Pattern Recognition Letters
volume
80
pages
7 pages
publisher
Elsevier
external identifiers
  • Scopus:84969498319
  • WOS:000382312200035
ISSN
0167-8655
DOI
10.1016/j.patrec.2016.05.001
language
English
LU publication?
yes
id
598e60a5-3d7b-4181-8e16-507622ca571d
date added to LUP
2016-10-17 08:37:13
date last changed
2017-02-02 10:25:57
@article{598e60a5-3d7b-4181-8e16-507622ca571d,
  abstract     = {<p>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.</p>},
  author       = {Alvén, Jennifer and Norlén, Alexander and Enqvist, Olof and Kahl, Fredrik},
  issn         = {0167-8655},
  keyword      = {Brain segmentation,Feature-based registration,Multi-atlas segmentation,Pericardium segmentation},
  language     = {eng},
  month        = {09},
  pages        = {249--255},
  publisher    = {Elsevier},
  series       = {Pattern Recognition Letters},
  title        = {Überatlas : Fast and robust registration for multi-atlas segmentation},
  url          = {http://dx.doi.org/10.1016/j.patrec.2016.05.001},
  volume       = {80},
  year         = {2016},
}