Emphysema quantification on cardiac CT scans using hidden markov measure field model: The MESA lung study

Jie Yang; Elsa D. Angelini; Pallavi P. Balte; Eric A. Hoffman; Colin O. Wu; Bharath Ambale Venkatesh; R. Graham Barr; Andrew F. Laine

doi:10.1007/978-3-319-46723-8_72

Emphysema quantification on cardiac CT scans using hidden markov measure field model: The MESA lung study

Jie Yang, Elsa D. Angelini, Pallavi P. Balte, Eric A. Hoffman, Colin O. Wu, Bharath Ambale Venkatesh, R. Graham Barr, Andrew F. Laine

School of Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Cardiac computed tomography (CT) scans include approximately 2/3 of the lung and can be obtained with low radiation exposure. Large cohorts of population-based research studies reported high correlations of emphysema quantification between full-lung (FL) and cardiac CT scans,using thresholding-based measurements. This work extends a hidden Markov measure field (HMMF) model-based segmentation method for automated emphysema quantification on cardiac CT scans. We show that the HMMF-based method,when compared with several types of thresholding,provides more reproducible emphysema segmentation on repeated cardiac scans,and more consistent measurements between longitudinal cardiac and FL scans from a diverse pool of scanner types and thousands of subjects with ten thousands of scans.

Original language	English (US)
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
Publisher	Springer Verlag
Pages	624-631
Number of pages	8
Volume	9901 LNCS
ISBN (Print)	9783319467221
DOIs	https://doi.org/10.1007/978-3-319-46723-8_72
State	Published - 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9901 LNCS
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Fingerprint

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Cite this

Yang, J., Angelini, E. D., Balte, P. P., Hoffman, E. A., Wu, C. O., Ambale Venkatesh, B., Barr, R. G., & Laine, A. F. (2016). Emphysema quantification on cardiac CT scans using hidden markov measure field model: The MESA lung study. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (Vol. 9901 LNCS, pp. 624-631). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9901 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46723-8_72

Emphysema quantification on cardiac CT scans using hidden markov measure field model : The MESA lung study. / Yang, Jie; Angelini, Elsa D.; Balte, Pallavi P.; Hoffman, Eric A.; Wu, Colin O.; Ambale Venkatesh, Bharath; Barr, R. Graham; Laine, Andrew F.

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Vol. 9901 LNCS Springer Verlag, 2016. p. 624-631 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9901 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yang, J, Angelini, ED, Balte, PP, Hoffman, EA, Wu, CO, Ambale Venkatesh, B, Barr, RG & Laine, AF 2016, Emphysema quantification on cardiac CT scans using hidden markov measure field model: The MESA lung study. in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. vol. 9901 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9901 LNCS, Springer Verlag, pp. 624-631. https://doi.org/10.1007/978-3-319-46723-8_72

Yang J, Angelini ED, Balte PP, Hoffman EA, Wu CO, Ambale Venkatesh B et al. Emphysema quantification on cardiac CT scans using hidden markov measure field model: The MESA lung study. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Vol. 9901 LNCS. Springer Verlag. 2016. p. 624-631. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-46723-8_72

Yang, Jie ; Angelini, Elsa D. ; Balte, Pallavi P. ; Hoffman, Eric A. ; Wu, Colin O. ; Ambale Venkatesh, Bharath ; Barr, R. Graham ; Laine, Andrew F. / Emphysema quantification on cardiac CT scans using hidden markov measure field model : The MESA lung study. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Vol. 9901 LNCS Springer Verlag, 2016. pp. 624-631 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{50953a241565437aaf1cf508c5a54060,

title = "Emphysema quantification on cardiac CT scans using hidden markov measure field model: The MESA lung study",

abstract = "Cardiac computed tomography (CT) scans include approximately 2/3 of the lung and can be obtained with low radiation exposure. Large cohorts of population-based research studies reported high correlations of emphysema quantification between full-lung (FL) and cardiac CT scans,using thresholding-based measurements. This work extends a hidden Markov measure field (HMMF) model-based segmentation method for automated emphysema quantification on cardiac CT scans. We show that the HMMF-based method,when compared with several types of thresholding,provides more reproducible emphysema segmentation on repeated cardiac scans,and more consistent measurements between longitudinal cardiac and FL scans from a diverse pool of scanner types and thousands of subjects with ten thousands of scans.",

author = "Jie Yang and Angelini, {Elsa D.} and Balte, {Pallavi P.} and Hoffman, {Eric A.} and Wu, {Colin O.} and {Ambale Venkatesh}, Bharath and Barr, {R. Graham} and Laine, {Andrew F.}",

year = "2016",

doi = "10.1007/978-3-319-46723-8_72",

language = "English (US)",

isbn = "9783319467221",

volume = "9901 LNCS",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "624--631",

booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings",

}

TY - GEN

T1 - Emphysema quantification on cardiac CT scans using hidden markov measure field model

T2 - The MESA lung study

AU - Yang, Jie

AU - Angelini, Elsa D.

AU - Balte, Pallavi P.

AU - Hoffman, Eric A.

AU - Wu, Colin O.

AU - Ambale Venkatesh, Bharath

AU - Barr, R. Graham

AU - Laine, Andrew F.

PY - 2016

Y1 - 2016

N2 - Cardiac computed tomography (CT) scans include approximately 2/3 of the lung and can be obtained with low radiation exposure. Large cohorts of population-based research studies reported high correlations of emphysema quantification between full-lung (FL) and cardiac CT scans,using thresholding-based measurements. This work extends a hidden Markov measure field (HMMF) model-based segmentation method for automated emphysema quantification on cardiac CT scans. We show that the HMMF-based method,when compared with several types of thresholding,provides more reproducible emphysema segmentation on repeated cardiac scans,and more consistent measurements between longitudinal cardiac and FL scans from a diverse pool of scanner types and thousands of subjects with ten thousands of scans.

AB - Cardiac computed tomography (CT) scans include approximately 2/3 of the lung and can be obtained with low radiation exposure. Large cohorts of population-based research studies reported high correlations of emphysema quantification between full-lung (FL) and cardiac CT scans,using thresholding-based measurements. This work extends a hidden Markov measure field (HMMF) model-based segmentation method for automated emphysema quantification on cardiac CT scans. We show that the HMMF-based method,when compared with several types of thresholding,provides more reproducible emphysema segmentation on repeated cardiac scans,and more consistent measurements between longitudinal cardiac and FL scans from a diverse pool of scanner types and thousands of subjects with ten thousands of scans.

UR - http://www.scopus.com/inward/record.url?scp=84996562207&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84996562207&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-46723-8_72

DO - 10.1007/978-3-319-46723-8_72

M3 - Conference contribution

C2 - 28845485

AN - SCOPUS:84996562207

SN - 9783319467221

VL - 9901 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 624

EP - 631

BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings

PB - Springer Verlag

ER -

Access to Document

10.1007/978-3-319-46723-8_72