Feature-space transformation improves supervised segmentation across scanners

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Feature-space transformation improves supervised segmentation across scanners. / van Opbroek, Annegreet; Achterberg, Hakim C.; de Bruijne, Marleen.

Machine learning meets medical imaging: First International Workshop, MLMMI 2015, Held in Conjunction with ICML 2015, Lille, France, July 11, 2015, Revised Selected Papers. Springer, 2015. p. 85-93 (Lecture notes in computer science, Vol. 9487).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

van Opbroek, A, Achterberg, HC & de Bruijne, M 2015, Feature-space transformation improves supervised segmentation across scanners. in Machine learning meets medical imaging: First International Workshop, MLMMI 2015, Held in Conjunction with ICML 2015, Lille, France, July 11, 2015, Revised Selected Papers. Springer, Lecture notes in computer science, vol. 9487, pp. 85-93, 1st International Workshop on Machine Learning Meets Medical Imaging, MLMMI 2015, Lille, France, 11/07/2015. https://doi.org/10.1007/978-3-319-27929-9_9

APA

van Opbroek, A., Achterberg, H. C., & de Bruijne, M. (2015). Feature-space transformation improves supervised segmentation across scanners. In Machine learning meets medical imaging: First International Workshop, MLMMI 2015, Held in Conjunction with ICML 2015, Lille, France, July 11, 2015, Revised Selected Papers (pp. 85-93). Springer. Lecture notes in computer science Vol. 9487 https://doi.org/10.1007/978-3-319-27929-9_9

Vancouver

van Opbroek A, Achterberg HC, de Bruijne M. Feature-space transformation improves supervised segmentation across scanners. In Machine learning meets medical imaging: First International Workshop, MLMMI 2015, Held in Conjunction with ICML 2015, Lille, France, July 11, 2015, Revised Selected Papers. Springer. 2015. p. 85-93. (Lecture notes in computer science, Vol. 9487). https://doi.org/10.1007/978-3-319-27929-9_9

Author

van Opbroek, Annegreet ; Achterberg, Hakim C. ; de Bruijne, Marleen. / Feature-space transformation improves supervised segmentation across scanners. Machine learning meets medical imaging: First International Workshop, MLMMI 2015, Held in Conjunction with ICML 2015, Lille, France, July 11, 2015, Revised Selected Papers. Springer, 2015. pp. 85-93 (Lecture notes in computer science, Vol. 9487).

Bibtex

@inproceedings{361ffa3cabb94484be23f2b79267bd45,
title = "Feature-space transformation improves supervised segmentation across scanners",
abstract = "Image-segmentation techniques based on supervised classification generally perform well on the condition that training and test samples have the same feature distribution. However, if training and test images are acquired with different scanners or scanning parameters, their feature distributions can be very different, which can hurt the performance of such techniques. We propose a feature-space-transformation method to overcome these differences in feature distributions. Our method learns a mapping of the feature values of training voxels to values observed in images from the test scanner. This transformation is learned from unlabeled images of subjects scanned on both the training scanner and the test scanner. We evaluated our method on hippocampus segmentation on 27 images of the Harmonized Hippocampal Protocol (HarP), a heterogeneous dataset consisting of 1.5T and 3T MR images. The results showed that our feature space transformation improved the Dice overlap of segmentations obtained with an SVM classifier from 0.36 to 0.85 when only 10 atlases were used and from 0.79 to 0.85 when around 100 atlases were used.",
keywords = "Brain, Hippocampus, Machine learning, MRI, Transfer learning",
author = "{van Opbroek}, Annegreet and Achterberg, {Hakim C.} and {de Bruijne}, Marleen",
year = "2015",
doi = "10.1007/978-3-319-27929-9_9",
language = "English",
series = "Lecture notes in computer science",
publisher = "Springer",
pages = "85--93",
booktitle = "Machine learning meets medical imaging",
address = "Switzerland",
note = "1st International Workshop on Machine Learning Meets Medical Imaging, MLMMI 2015 ; Conference date: 11-07-2015 Through 11-07-2015",

}

RIS

TY - GEN

T1 - Feature-space transformation improves supervised segmentation across scanners

AU - van Opbroek, Annegreet

AU - Achterberg, Hakim C.

AU - de Bruijne, Marleen

PY - 2015

Y1 - 2015

N2 - Image-segmentation techniques based on supervised classification generally perform well on the condition that training and test samples have the same feature distribution. However, if training and test images are acquired with different scanners or scanning parameters, their feature distributions can be very different, which can hurt the performance of such techniques. We propose a feature-space-transformation method to overcome these differences in feature distributions. Our method learns a mapping of the feature values of training voxels to values observed in images from the test scanner. This transformation is learned from unlabeled images of subjects scanned on both the training scanner and the test scanner. We evaluated our method on hippocampus segmentation on 27 images of the Harmonized Hippocampal Protocol (HarP), a heterogeneous dataset consisting of 1.5T and 3T MR images. The results showed that our feature space transformation improved the Dice overlap of segmentations obtained with an SVM classifier from 0.36 to 0.85 when only 10 atlases were used and from 0.79 to 0.85 when around 100 atlases were used.

AB - Image-segmentation techniques based on supervised classification generally perform well on the condition that training and test samples have the same feature distribution. However, if training and test images are acquired with different scanners or scanning parameters, their feature distributions can be very different, which can hurt the performance of such techniques. We propose a feature-space-transformation method to overcome these differences in feature distributions. Our method learns a mapping of the feature values of training voxels to values observed in images from the test scanner. This transformation is learned from unlabeled images of subjects scanned on both the training scanner and the test scanner. We evaluated our method on hippocampus segmentation on 27 images of the Harmonized Hippocampal Protocol (HarP), a heterogeneous dataset consisting of 1.5T and 3T MR images. The results showed that our feature space transformation improved the Dice overlap of segmentations obtained with an SVM classifier from 0.36 to 0.85 when only 10 atlases were used and from 0.79 to 0.85 when around 100 atlases were used.

KW - Brain

KW - Hippocampus

KW - Machine learning

KW - MRI

KW - Transfer learning

U2 - 10.1007/978-3-319-27929-9_9

DO - 10.1007/978-3-319-27929-9_9

M3 - Article in proceedings

AN - SCOPUS:84955254180

T3 - Lecture notes in computer science

SP - 85

EP - 93

BT - Machine learning meets medical imaging

PB - Springer

T2 - 1st International Workshop on Machine Learning Meets Medical Imaging, MLMMI 2015

Y2 - 11 July 2015 through 11 July 2015

ER -

ID: 154368998