Asymmetric similarity-weighted ensembles for image segmentation

TY - GEN

T1 - Asymmetric similarity-weighted ensembles for image segmentation

AU - Cheplygina, V.

AU - Van Opbroek, A.

AU - Ikram, M. A.

AU - Vernooij, M. W.

AU - de Bruijne, Marleen

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - asymmetry

KW - similarity measure

KW - tissue segmentation

KW - Transfer learning

U2 - 10.1109/ISBI.2016.7493262

DO - 10.1109/ISBI.2016.7493262

M3 - Article in proceedings

AN - SCOPUS:84978434492

SP - 273

EP - 277

BT - 2016 IEEE International Symposium on Biomedical Imaging

PB - IEEE

T2 - 2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016

Y2 - 13 April 2016 through 16 April 2016

ER -