Asymmetric similarity-weighted ensembles for image segmentation

Conference paper (2016)

Authors

Veronika Cheplygina Pattern Recognition and Bioinformatics - EEMCS
V.V. Cheplygina Pattern Recognition and Bioinformatics - EEMCS
A van Opbroek Erasmus Medical Centre
MA Ikram Erasmus Medical Centre
M.W. Vernooij Erasmus Medical Centre
M de Bruijne University of Copenhagen
M de de Bruijne University of Copenhagen
M. de Bruijne University of Copenhagen
M. Bruijne University of Copenhagen
M Bruijne University of Copenhagen
M de Bruijne University of Copenhagen
Research Group
Pattern Recognition and Bioinformatics (EEMCS) (TU Delft)
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Published Date

2016

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

Abstract

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.