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Extraction of Airways with Probabilistic State-Space Models and Bayesian Smoothing. / Raghavendra, Selvan; Petersen, Jens; Pedersen, Jesper Johannes Holst; de Bruijne, Marleen.
Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics: First International Workshop, GRAIL 2017, 6th International Workshop, MFCA 2017, and Third International Workshop, MICGen 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 10–14, 2017, Proceedings. ed. / M. Jorge Cardoso; Tal Arbel; Enzo Ferrante; Xavier Pennec; Adrian V. Dalca; Sarah Parisot; Sarang Joshi; Nematollah K. Batmanghelich; Aristeidis Sotiras; Mads Nielsen; Mert R. Sabuncu; Tom Fletcher; Li Shen; Stanley Durrleman; Stefan Sommer. Springer, 2017. p. 53-63 (Lecture notes in computer science, Vol. 10551).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Raghavendra, S, Petersen, J, Pedersen, JJH
& de Bruijne, M 2017,
Extraction of Airways with Probabilistic State-Space Models and Bayesian Smoothing. in MJ Cardoso, T Arbel, E Ferrante, X Pennec, AV Dalca, S Parisot, S Joshi, NK Batmanghelich, A Sotiras, M Nielsen, MR Sabuncu, T Fletcher, L Shen, S Durrleman & S Sommer (eds),
Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics: First International Workshop, GRAIL 2017, 6th International Workshop, MFCA 2017, and Third International Workshop, MICGen 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 10–14, 2017, Proceedings. Springer, Lecture notes in computer science, vol. 10551, pp. 53-63, 1st International Workshop on Graphs in Biomedical Image Analysis (GRAIL) / 6th International Workshop on Mathematical Foundations of Computational Anatomy (MFCA) / 3rd International Workshop on Imaging Genetics (MICGen), Quebec, Canada,
10/09/2017.
https://doi.org/10.1007/978-3-319-67675-3_6
APA
Raghavendra, S., Petersen, J., Pedersen, J. J. H.
, & de Bruijne, M. (2017).
Extraction of Airways with Probabilistic State-Space Models and Bayesian Smoothing. In M. J. Cardoso, T. Arbel, E. Ferrante, X. Pennec, A. V. Dalca, S. Parisot, S. Joshi, N. K. Batmanghelich, A. Sotiras, M. Nielsen, M. R. Sabuncu, T. Fletcher, L. Shen, S. Durrleman, & S. Sommer (Eds.),
Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics: First International Workshop, GRAIL 2017, 6th International Workshop, MFCA 2017, and Third International Workshop, MICGen 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 10–14, 2017, Proceedings (pp. 53-63). Springer. Lecture notes in computer science Vol. 10551
https://doi.org/10.1007/978-3-319-67675-3_6
Vancouver
Raghavendra S, Petersen J, Pedersen JJH
, de Bruijne M.
Extraction of Airways with Probabilistic State-Space Models and Bayesian Smoothing. In Cardoso MJ, Arbel T, Ferrante E, Pennec X, Dalca AV, Parisot S, Joshi S, Batmanghelich NK, Sotiras A, Nielsen M, Sabuncu MR, Fletcher T, Shen L, Durrleman S, Sommer S, editors, Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics: First International Workshop, GRAIL 2017, 6th International Workshop, MFCA 2017, and Third International Workshop, MICGen 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 10–14, 2017, Proceedings. Springer. 2017. p. 53-63. (Lecture notes in computer science, Vol. 10551).
https://doi.org/10.1007/978-3-319-67675-3_6
Author
Raghavendra, Selvan ; Petersen, Jens ; Pedersen, Jesper Johannes Holst ; de Bruijne, Marleen. / Extraction of Airways with Probabilistic State-Space Models and Bayesian Smoothing. Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics: First International Workshop, GRAIL 2017, 6th International Workshop, MFCA 2017, and Third International Workshop, MICGen 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 10–14, 2017, Proceedings. editor / M. Jorge Cardoso ; Tal Arbel ; Enzo Ferrante ; Xavier Pennec ; Adrian V. Dalca ; Sarah Parisot ; Sarang Joshi ; Nematollah K. Batmanghelich ; Aristeidis Sotiras ; Mads Nielsen ; Mert R. Sabuncu ; Tom Fletcher ; Li Shen ; Stanley Durrleman ; Stefan Sommer. Springer, 2017. pp. 53-63 (Lecture notes in computer science, Vol. 10551).
Bibtex
@inproceedings{13c0f3b49f82431d8ee4d2cd9a3e9ae8,
title = "Extraction of Airways with Probabilistic State-Space Models and Bayesian Smoothing",
abstract = "Segmenting tree structures is common in several image processing applications. In medical image analysis, reliable segmentations of airways, vessels, neurons and other tree structures can enable important clinical. applications. We present a framework for tracking tree structures comprising of elongated branches using probabilistic state-space models and Bayesian smoothing. Unlike most existing methods that proceed with sequential tracking of branches, we present an exploratory method, that is less sensitive to local anomalies in the data due to acquisition noise and/or interfering structures. The evolution of individual branches is modelled using a process model and the observed data is incorporated into the update step of the Bayesian smoother using a measurement model that is based on a multi-scale blob detector. Bayesian smoothing is performed using the RTS (Rauch-Tung-Striebel) smoother, which provides Gaussian density estimates of branch states at each tracking step. We select likely branch seed points automatically based on the response of the blob detection and track from all such seed points using the RTS smoother. We use covariance of the marginal posterior density estimated for each branch to discriminate false positive and true positive branches. The method is evaluated on 3D chest CT scans to track airways. We show that the presented method results in additional branches compared to a baseline method based on region growing on probability images",
keywords = "Probabilistic state-space, Bayesian smoothing, Tree segmentation, Airways, CT, SEGMENTATION",
author = "Selvan Raghavendra and Jens Petersen and Pedersen, {Jesper Johannes Holst} and {de Bruijne}, Marleen",
year = "2017",
doi = "10.1007/978-3-319-67675-3_6",
language = "English",
isbn = "978-3-319-67674-6",
series = "Lecture notes in computer science",
publisher = "Springer",
pages = "53--63",
editor = "Cardoso, {M. Jorge} and Tal Arbel and Enzo Ferrante and Xavier Pennec and Dalca, {Adrian V.} and Sarah Parisot and Sarang Joshi and Batmanghelich, {Nematollah K.} and Aristeidis Sotiras and Mads Nielsen and Sabuncu, {Mert R.} and Tom Fletcher and Li Shen and Stanley Durrleman and Stefan Sommer",
booktitle = "Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics",
address = "Switzerland",
note = "1st International Workshop on Graphs in Biomedical Image Analysis (GRAIL) / 6th International Workshop on Mathematical Foundations of Computational Anatomy (MFCA) / 3rd International Workshop on Imaging Genetics (MICGen) ; Conference date: 10-09-2017 Through 14-09-2017",
}
RIS
TY - GEN
T1 - Extraction of Airways with Probabilistic State-Space Models and Bayesian Smoothing
AU - Raghavendra, Selvan
AU - Petersen, Jens
AU - Pedersen, Jesper Johannes Holst
AU - de Bruijne, Marleen
PY - 2017
Y1 - 2017
N2 - Segmenting tree structures is common in several image processing applications. In medical image analysis, reliable segmentations of airways, vessels, neurons and other tree structures can enable important clinical. applications. We present a framework for tracking tree structures comprising of elongated branches using probabilistic state-space models and Bayesian smoothing. Unlike most existing methods that proceed with sequential tracking of branches, we present an exploratory method, that is less sensitive to local anomalies in the data due to acquisition noise and/or interfering structures. The evolution of individual branches is modelled using a process model and the observed data is incorporated into the update step of the Bayesian smoother using a measurement model that is based on a multi-scale blob detector. Bayesian smoothing is performed using the RTS (Rauch-Tung-Striebel) smoother, which provides Gaussian density estimates of branch states at each tracking step. We select likely branch seed points automatically based on the response of the blob detection and track from all such seed points using the RTS smoother. We use covariance of the marginal posterior density estimated for each branch to discriminate false positive and true positive branches. The method is evaluated on 3D chest CT scans to track airways. We show that the presented method results in additional branches compared to a baseline method based on region growing on probability images
AB - Segmenting tree structures is common in several image processing applications. In medical image analysis, reliable segmentations of airways, vessels, neurons and other tree structures can enable important clinical. applications. We present a framework for tracking tree structures comprising of elongated branches using probabilistic state-space models and Bayesian smoothing. Unlike most existing methods that proceed with sequential tracking of branches, we present an exploratory method, that is less sensitive to local anomalies in the data due to acquisition noise and/or interfering structures. The evolution of individual branches is modelled using a process model and the observed data is incorporated into the update step of the Bayesian smoother using a measurement model that is based on a multi-scale blob detector. Bayesian smoothing is performed using the RTS (Rauch-Tung-Striebel) smoother, which provides Gaussian density estimates of branch states at each tracking step. We select likely branch seed points automatically based on the response of the blob detection and track from all such seed points using the RTS smoother. We use covariance of the marginal posterior density estimated for each branch to discriminate false positive and true positive branches. The method is evaluated on 3D chest CT scans to track airways. We show that the presented method results in additional branches compared to a baseline method based on region growing on probability images
KW - Probabilistic state-space
KW - Bayesian smoothing
KW - Tree segmentation
KW - Airways
KW - CT
KW - SEGMENTATION
U2 - 10.1007/978-3-319-67675-3_6
DO - 10.1007/978-3-319-67675-3_6
M3 - Article in proceedings
SN - 978-3-319-67674-6
T3 - Lecture notes in computer science
SP - 53
EP - 63
BT - Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics
A2 - Cardoso, M. Jorge
A2 - Arbel, Tal
A2 - Ferrante, Enzo
A2 - Pennec, Xavier
A2 - Dalca, Adrian V.
A2 - Parisot, Sarah
A2 - Joshi, Sarang
A2 - Batmanghelich, Nematollah K.
A2 - Sotiras, Aristeidis
A2 - Nielsen, Mads
A2 - Sabuncu, Mert R.
A2 - Fletcher, Tom
A2 - Shen, Li
A2 - Durrleman, Stanley
A2 - Sommer, Stefan
PB - Springer
T2 - 1st International Workshop on Graphs in Biomedical Image Analysis (GRAIL) / 6th International Workshop on Mathematical Foundations of Computational Anatomy (MFCA) / 3rd International Workshop on Imaging Genetics (MICGen)
Y2 - 10 September 2017 through 14 September 2017
ER -
