Extraction of airways with probabilistic state-space models and Bayesian smoothing

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

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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. red. / 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. s. 53-63 (Lecture notes in computer science, Bind 10551).

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Harvard

Raghavendra, S, Petersen, J, Pedersen, JJH & de Bruijne, M 2017, Extraction of airways with probabilistic state-space models and Bayesian smoothing. i 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 (red), 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, bind 10551, s. 53-63, 1st International Workshop on Graphs in Biomedical Image Analysis, Québec City, 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. I 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 (red.), 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 (s. 53-63). Springer. Lecture notes in computer science, Bind. 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. I 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, red., 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. s. 53-63. (Lecture notes in computer science, Bind 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. red. / 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. s. 53-63 (Lecture notes in computer science, Bind 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",
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",
note = "null ; 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

N1 - Conference code: 1

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

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

Y2 - 10 September 2017 through 14 September 2017

ER -

ID: 184144076