Asymmetric similarity-weighted ensembles for image segmentation

Veronika Cheplygina, A van Opbroek, MA Ikram, M.W. Vernooij, M de Bruijne

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

5 Citations (Scopus)


Supervised classification is widely used for image segmentation. To work effectively, these techniques need large amounts of labeled training data, that is representative of the test data. Different patient groups, different scanners or different scanning protocols can lead to differences between the images, thus representative data might not be available. Transfer learning techniques can be used to account for these differences, thus taking advantage of all the available data acquired with different protocols. We investigate the use of classifier ensembles, where each classifier is weighted according to the similarity between the data it is trained on, and the data it needs to segment. We examine 3 asymmetric similarity measures that can be used in scenarios where no labeled data from a newly introduced scanner or scanning protocol is available. We show that the asymmetry is informative and the direction of measurement needs to be chosen carefully. We also show that a point set similarity measure is robust across different studies, and outperforms state-of-the-art results on a multi-center brain tissue segmentation task.
Original languageEnglish
Title of host publication2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)
Place of PublicationPiscataway
Number of pages5
ISBN (Electronic)978-1-4799-2349-6
Publication statusPublished - 2016
Event2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Prague, Czech Republic
Duration: 13 Apr 201616 Apr 2016


Conference2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016
CountryCzech Republic


  • Transfer learning
  • similarity measure
  • asymmetry
  • tissue segmentation

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