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Überatlas: Fast and robust registration for multi-atlas segmentation

Jennifer Alvén (Institutionen för signaler och system, Bildanalys och datorseende) ; Alexander Norlén ; Olof Enqvist (Institutionen för signaler och system, Bildanalys och datorseende) ; Fredrik Kahl (Institutionen för signaler och system, Bildanalys och datorseende)
Pattern Recognition Letters (0167-8655). Vol. 80 (2016), p. 249-255.
[Artikel, refereegranskad vetenskaplig]

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.

Nyckelord: Feature-based registration; Multi-atlas segmentation; Pericardium segmentation; Brain segmentation

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Denna post skapades 2016-05-25. Senast ändrad 2016-09-30.
CPL Pubid: 236924


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Institutioner (Chalmers)

Institutionen för signaler och system, Bildanalys och datorseende (2013-2017)


Informations- och kommunikationsteknik
Data- och informationsvetenskap
Datorseende och robotik (autonoma system)
Medicinsk bildbehandling

Chalmers infrastruktur

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Improving Multi-Atlas Segmentation Methods for Medical Images