Transfer learning by feature-space transformation: A method for Hippocampus segmentation across scanners
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Transfer learning by feature-space transformation : A method for Hippocampus segmentation across scanners. / van Opbroek, Annegreet; Achterberg, Hakim C; Vernooij, Meike W; Arfan Ikram, M; de Bruijne, Marleen.
In: NeuroImage: Clinical, Vol. 20, 2018, p. 466-475.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Transfer learning by feature-space transformation
T2 - A method for Hippocampus segmentation across scanners
AU - van Opbroek, Annegreet
AU - Achterberg, Hakim C
AU - Vernooij, Meike W
AU - Arfan Ikram, M
AU - de Bruijne, Marleen
PY - 2018
Y1 - 2018
N2 - Many successful approaches in MR brain segmentation use supervised voxel classification, which requires manually labeled training images that are representative of the test images to segment. However, the performance of such methods often deteriorates if training and test images are acquired with different scanners or scanning parameters, since this leads to differences in feature representations between training and test data. In this paper we propose a feature-space transformation (FST) to overcome such differences in feature representations. The proposed FST is derived from unlabeled images of a subject that was scanned with both the source and the target scan protocol. After an affine registration, these images give a mapping between source and target voxels in the feature space. This mapping is then used to map all training samples to the feature representation of the test samples. We evaluated the benefit of the proposed FST on hippocampus segmentation. Experiments were performed on two datasets: one with relatively small differences between training and test images and one with large differences. In both cases, the FST significantly improved the performance compared to using only image normalization. Additionally, we showed that our FST can be used to improve the performance of a state-of-the-art patch-based-atlas-fusion technique in case of large differences between scanners.
AB - Many successful approaches in MR brain segmentation use supervised voxel classification, which requires manually labeled training images that are representative of the test images to segment. However, the performance of such methods often deteriorates if training and test images are acquired with different scanners or scanning parameters, since this leads to differences in feature representations between training and test data. In this paper we propose a feature-space transformation (FST) to overcome such differences in feature representations. The proposed FST is derived from unlabeled images of a subject that was scanned with both the source and the target scan protocol. After an affine registration, these images give a mapping between source and target voxels in the feature space. This mapping is then used to map all training samples to the feature representation of the test samples. We evaluated the benefit of the proposed FST on hippocampus segmentation. Experiments were performed on two datasets: one with relatively small differences between training and test images and one with large differences. In both cases, the FST significantly improved the performance compared to using only image normalization. Additionally, we showed that our FST can be used to improve the performance of a state-of-the-art patch-based-atlas-fusion technique in case of large differences between scanners.
KW - Classification
KW - Domain adaptation
KW - Hippocampus
KW - MRI
KW - Segmentation
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85051644174&partnerID=8YFLogxK
U2 - 10.1016/j.nicl.2018.08.005
DO - 10.1016/j.nicl.2018.08.005
M3 - Journal article
C2 - 30128285
VL - 20
SP - 466
EP - 475
JO - NeuroImage: Clinical
JF - NeuroImage: Clinical
SN - 2213-1582
ER -
ID: 201579400