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A Cross-Center Smoothness Prior for Variational Bayesian Brain Tissue Segmentation. / Kouw, Wouter M.; Ørting, Silas N.; Petersen, Jens; Pedersen, Kim S.; de Bruijne, Marleen.
Information Processing in Medical Imaging : 26th International Conference, IPMI 2019, Proceedings. red. / Siqi Bao; Albert C.S. Chung; James C. Gee; Paul A. Yushkevich. Springer, 2019. s. 360-371 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11492 LNCS).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Kouw, WM, Ørting, SN
, Petersen, J, Pedersen, KS & de Bruijne, M 2019,
A Cross-Center Smoothness Prior for Variational Bayesian Brain Tissue Segmentation. i S Bao, ACS Chung, JC Gee & PA Yushkevich (red),
Information Processing in Medical Imaging : 26th International Conference, IPMI 2019, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), bind 11492 LNCS, s. 360-371, 26th International Conference on Information Processing in Medical Imaging, IPMI 2019, Hong Kong, Kina,
02/06/2019.
https://doi.org/10.1007/978-3-030-20351-1_27APA
Kouw, W. M., Ørting, S. N.
, Petersen, J., Pedersen, K. S., & de Bruijne, M. (2019).
A Cross-Center Smoothness Prior for Variational Bayesian Brain Tissue Segmentation. I S. Bao, A. C. S. Chung, J. C. Gee, & P. A. Yushkevich (red.),
Information Processing in Medical Imaging : 26th International Conference, IPMI 2019, Proceedings (s. 360-371). Springer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Bind 11492 LNCS
https://doi.org/10.1007/978-3-030-20351-1_27Vancouver
Kouw WM, Ørting SN
, Petersen J, Pedersen KS, de Bruijne M.
A Cross-Center Smoothness Prior for Variational Bayesian Brain Tissue Segmentation. I Bao S, Chung ACS, Gee JC, Yushkevich PA, red., Information Processing in Medical Imaging : 26th International Conference, IPMI 2019, Proceedings. Springer. 2019. s. 360-371. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11492 LNCS).
https://doi.org/10.1007/978-3-030-20351-1_27Author
Kouw, Wouter M. ; Ørting, Silas N. ; Petersen, Jens ; Pedersen, Kim S. ; de Bruijne, Marleen. / A Cross-Center Smoothness Prior for Variational Bayesian Brain Tissue Segmentation. Information Processing in Medical Imaging : 26th International Conference, IPMI 2019, Proceedings. red. / Siqi Bao ; Albert C.S. Chung ; James C. Gee ; Paul A. Yushkevich. Springer, 2019. s. 360-371 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11492 LNCS).
Bibtex
@inproceedings{ddec8ff45de54d58941e968129232891,
title = "A Cross-Center Smoothness Prior for Variational Bayesian Brain Tissue Segmentation",
abstract = "Suppose one is faced with the challenge of tissue segmentation in MR images, without annotators at their center to provide labeled training data. One option is to go to another medical center for a trained classifier. Sadly, tissue classifiers do not generalize well across centers due to voxel intensity shifts caused by center-specific acquisition protocols. However, certain aspects of segmentations, such as spatial smoothness, remain relatively consistent and can be learned separately. Here we present a smoothness prior that is fit to segmentations produced at another medical center. This informative prior is presented to an unsupervised Bayesian model. The model clusters the voxel intensities, such that it produces segmentations that are similarly smooth to those of the other medical center. In addition, the unsupervised Bayesian model is extended to a semi-supervised variant, which needs no visual interpretation of clusters into tissues.",
keywords = "Bayesian transfer learning, Image segmentation, Variational inference",
author = "Kouw, {Wouter M.} and {\O}rting, {Silas N.} and Jens Petersen and Pedersen, {Kim S.} and {de Bruijne}, Marleen",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-3-030-20351-1_27",
language = "English",
isbn = "9783030203504",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "360--371",
editor = "Siqi Bao and Chung, {Albert C.S.} and Gee, {James C.} and Yushkevich, {Paul A.}",
booktitle = "Information Processing in Medical Imaging",
address = "Switzerland",
note = "26th International Conference on Information Processing in Medical Imaging, IPMI 2019 ; Conference date: 02-06-2019 Through 07-06-2019",
}
RIS
TY - GEN
T1 - A Cross-Center Smoothness Prior for Variational Bayesian Brain Tissue Segmentation
AU - Kouw, Wouter M.
AU - Ørting, Silas N.
AU - Petersen, Jens
AU - Pedersen, Kim S.
AU - de Bruijne, Marleen
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Suppose one is faced with the challenge of tissue segmentation in MR images, without annotators at their center to provide labeled training data. One option is to go to another medical center for a trained classifier. Sadly, tissue classifiers do not generalize well across centers due to voxel intensity shifts caused by center-specific acquisition protocols. However, certain aspects of segmentations, such as spatial smoothness, remain relatively consistent and can be learned separately. Here we present a smoothness prior that is fit to segmentations produced at another medical center. This informative prior is presented to an unsupervised Bayesian model. The model clusters the voxel intensities, such that it produces segmentations that are similarly smooth to those of the other medical center. In addition, the unsupervised Bayesian model is extended to a semi-supervised variant, which needs no visual interpretation of clusters into tissues.
AB - Suppose one is faced with the challenge of tissue segmentation in MR images, without annotators at their center to provide labeled training data. One option is to go to another medical center for a trained classifier. Sadly, tissue classifiers do not generalize well across centers due to voxel intensity shifts caused by center-specific acquisition protocols. However, certain aspects of segmentations, such as spatial smoothness, remain relatively consistent and can be learned separately. Here we present a smoothness prior that is fit to segmentations produced at another medical center. This informative prior is presented to an unsupervised Bayesian model. The model clusters the voxel intensities, such that it produces segmentations that are similarly smooth to those of the other medical center. In addition, the unsupervised Bayesian model is extended to a semi-supervised variant, which needs no visual interpretation of clusters into tissues.
KW - Bayesian transfer learning
KW - Image segmentation
KW - Variational inference
U2 - 10.1007/978-3-030-20351-1_27
DO - 10.1007/978-3-030-20351-1_27
M3 - Article in proceedings
AN - SCOPUS:85066129813
SN - 9783030203504
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 360
EP - 371
BT - Information Processing in Medical Imaging
A2 - Bao, Siqi
A2 - Chung, Albert C.S.
A2 - Gee, James C.
A2 - Yushkevich, Paul A.
PB - Springer
T2 - 26th International Conference on Information Processing in Medical Imaging, IPMI 2019
Y2 - 2 June 2019 through 7 June 2019
ER -
