Label fusion in multi-atlas based segmentation with user-defined local weights

T.R. Langerak, U.A. Heide, van der, A.N.T.J. Kotte, F.F. Berendsen, J.P.W. Pluim

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)
1 Downloads (Pure)


Multi-atlas based segmentation is a popular method to automatically segment a target image, in which the correspondence to already segmented atlas images is used to construct multiple segmentations for a single structure in the target image. These multiple segmentations are then combined into a single segmentation for the target image in a process called label fusion. In the past, the result of multi-atlas based segmentation has mostly been evaluated using a volume overlap measure. However, such a measure can only be used to assess the global quality of a segmentation and does not take into account local differences in for example the clinical relevance of a certain region of the segmentation. We propose to use voxel-based weights in the evaluation of segmentations and show that by using these weights already during the label fusion process, one is able to obtain multi-atlas based segmentation results with an improved clinical relevance compared to unweighted atlas based segmentation. A method is proposed to implement this for multi-atlas based segmentation of the prostate. © 2011 IEEE.
Original languageEnglish
Title of host publicationProceedings of the 8th IEEE International Symposium on Biomedical Imaging : From Nano to Macro ( ISBI'11), 30 March 2011 through 2 April 2011, Chicago, IL
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Print)978-1-4244-4128-0
Publication statusPublished - 2011


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